Development of Value Creation Drivers for Sustainable Design of Green Buildings in Saudi Arabia

: The sustainability of green buildings has been widely recognized around the world in the recent past. Evaluating the investment on such buildings, with higher complexity than the conventional buildings, involves multiple and diverse stakeholders, such as consultants, contractors, general public, governmental institutions, etc. The selection of useful value creation drivers is a di ﬃ cult task while accommodating the opinion of a group of stakeholders with varying perceptions and experiences regarding the value creation in green building design and the associated costs. In this research, a framework is proposed to develop a set of the most important value creation drivers (VCDs) for green buildings. Five primary VCDs were developed to cover the ﬁnancial, functional, operational, environmental, and management aspects of a green building. Ninety-eight (98) performance value drivers (PVDs) were identiﬁed through the literature for assessing the performance of these value creation drivers. The identiﬁed PVDs were evaluated through a hand-delivered questionnaire survey, followed by detailed statistical analysis of the collected data while using Statistical Package for Social Sciences (SPSS) and Microsoft Excel software. Factor analyses were performed to eliminate the PVDs with least importance based on the responses obtained from 89 experienced managers (45%), engineers (38%), and architects (17%) working in the ﬁeld of value management of construction industry in Saudi Arabia. Finally, 51 most important PVDs were grouped into two clusters for each value creation driver; for instance, control and planning clusters to assess the performance of management’s VCD. The ﬁnal outcome of the research in the form of ﬁve top level VCDs, 10 clusters, and 51 PVDs will facilitate the designers for enhancing the performance e ﬃ ciency and value from investment for green buildings in Saudi Arabia, Gulf, and elsewhere.


Introduction
Modern construction works, such as green buildings, with increasing complexity and immensity involve a wide range of stakeholders, such as consultants, contractors, material suppliers, general public, governmental institutions, etc. Coordinating all of these parties simultaneously in the execution of a construction project is a big challenge [1]. Value management helps to improve communication between the project's parties, accommodate mutual understanding of the project objectives, provide better quality project definition, build innovation, and eliminate unnecessary cost [2]. Value can be understood in a multitude of dimensions through economic, cultural, and social interpretations [3]. The whole-life-cycle value exchange mechanisms are risky, complex, might have an effect on adding VFI2 Efficiency of operational expenditure (OPEX) VFI3 Maximise the cost efficiency to build VFI4 Deliver/achieve cost certainty VFI5 Improve economic efficiency VFI6 Increase economic lifetime VFI7 Consider state of inflation VFI8 Maximise return on capex VFI9 Return on investment VFI10 Create investment planning and asset allocation VFI11 Maximise residual value VFI12 Minimise cost of capital VFI13 Prevent legal and potential damages costs-provide adequate insurance cover to protect against legal and potential damages costs VFI14 Prevent loss of revenue VFI15 Optimise risk-return ratio of alternative options VFI16 Reduce the fees payable VFI17 Increase turnover VFI18 Maximise sale price VFI19 Maximise rental price VFI20 Maximise occupancy rate

VFU FUNCTIONAL PERFORMANCE VALUE DRIVERS
Connaughton and Green [2]; Kelly and Duerk [14]; NASA [19]; Thiry [23]; NAO [24]; Dell'Isola [25]; ICE [28]; Greenwood [32]; Austin [33]; Kelly and Male [34]; Muldavin [39]; Alyami et al. [43]; BEMU [44]; Pulaski [45]; Dryer [46]; Yates et al. [47]; Slaughter [48]; Kibert et al. [49]; Markeset and Kumar [50] VFU1 Maintain adaptable building-useful to all VFU2 Increase life of services VFU3 Provide function-fitness for purpose VFU4 Offer flexibility and the potential to cater for user changes in the future VFU5 Accommodate growth VFU6 Provide inherent possibilities and values in alternative uses VFU7 Increase ease of use VFU8 Increase efficiency-add capacity VFU9 Adequate size and efficiency (gross internal, net internal and net usable areas and ratio) VFU10 Achieve spatial quality VFU11 Allow for space allowance VFU12 Allow/ease of/control/secure accessibility VFU13 Provide disability access VFU14 Assure convenience VFU15 Provide durable building-last longer VFU16 Maintain durability VFU17 Enable buildability VFU18 Create reliable building-safer VFU19 Maintain security-health and safety VFU20 Suitability and maintainability of materials VFU21 Meet all statutory requirements and building regulations VFU22 Ensure designed elements are standardized VFU23 Configure design to enable an efficient construction process VFU24 Ensure construction efficiency is considered in specification VFU25 Reduce risk of failure Ensure electrical functional meet a satisfactory level of performance

VOP1
Reduce/minimise/save energy usage VOP2 Maintain efficiency in terms of energy VOP3 Increase efficiency of utilities VOP4 Increase efficiency of heating, cooling and lighting VOP5 Easy to clean VOP6 Easy to maintain VOP7 Easy to manage VOP8 Easy to operate VOP9 Easy to inspect and maintain VOP10 Ease of running and managing the building's equipment VOP11 Provide building systems that are easy to operate and control VOP12 Manage maintenance and servicing of equipment VOP13 Accommodate telecommunications VOP14 Provide security services VOP15 Reduce operational risk VOP16 Improve waste management-reducing and dealing with waste

VEN ENVIRONMENTAL PERFORMANCE VALUE DRIVERS
NASA [19]; NAO [24]; Davies [30]; Green Building Council [31] Austin [33]; Ostime [38]; Muldavin [39]; Goldberger [42]; Alyami et al. [43]; IGBC [52]; Inbuilt [53]; Sleeuw [54]; VEN1 Provide low carbon in use VEN2 Accommodate energy and carbon efficiency VEN3 Provide indoor environmental quality VEN4 Access to natural light, management of air quality and temperature VEN5 Increase use of natural ventilation VEN6 Ensure lighting and acoustic criteria for the facility design meet a satisfactory level of performance VEN7 Specifying low-maintenance, durable, environmentally preferable materials and equipment VEN8 Maximise resource reuse VEN9 Use renewable or recyclable resources VEN10 Minimise consumption of resources VEN11 Conserve water resources VEN12 Respond to site microclimate VEN13 Conform/adapt to future changes VEN14 Consider people and their local environment VEN15 Design for minimum waste VEN16 Obtain environmental certification from appropriate bodies

VMA1
Provide effective project management and delivery VMA2 Provide risk management VMA3 Create strategic planning VMA4 Choose an appropriate procurement approach VMA5 Provide cost control to achieve the project objectives VMA6 Produce effective plans to achieve the project objectives VMA7 Lead work design and delivery planning VMA8 Maximise organisational efficiency VMA9 Able to design to scope/cost/budget/schedule/quality VMA10 Able to construct to scope/cost/budget/schedule/quality VMA11 Completed to specification VMA12 Maintain stakeholder interaction-accountability/clear expectations

Financial Performance Value Drivers
Investigating and extracting the controllable financial value creation drivers (VFI) will help in attaining value engineering objectives by optimizing the financial investment and the project cost through examining the alternative options that were identified in the function analysis stage. A value management study needs to consider the capital expenditure, i.e, the investment costs incurred to complete the project and get the physical assets, and the operational expenditure, i.e., the ongoing cost required to operate the project and for further investigation that is required to understand and construct what the client wants [18]. The National Aeronautics and Space Administration (NASA) [19] suggests that it is important to find out the proper balance between the design/construction cost for a green building and the reduction in life cycle costing. Table 1 lists the VFI that need to be optimized during the design of green building assets. As per NAO [24], the financial performance for a project business case can be summarized as "optimizing the balance between the capital costs, operating costs and residual whole life value". Table 1 lists the list of important functional performance value drivers (VFU) that need to be used in optimizing VC during the design of green building assets. It has been postulated that achieving high green building asset function reliability would lead to an increase in the asset value in the business. Functional value is described as "an organized approach that is based on functional analysis which aims to obtain the essential functions at lowest cost within the required performance, reliability, quality and safety" [25].

Functional Performance Value Drivers
The NAO [24] stated that a well-designed building can increase the asset value across its life. The design costs are likely to be 0.3-0.5% of the total cost over the lifetime of a building, construction cost Sustainability 2019, 11, 5867 6 of 33 about 2-3% of the total, and the cost of running the public services about 85% of the total. Hence large benefits can be gained through efficient building designs in comparison to the spent cost throughout its life. Building functionality can be defined as "the arrangement, quality and interrelationship of spaces and how the building is designed to be useful to all" [24]. It has a high impact on the other value generators: financial saving, high operating efficiency, maintain save working environment, and effective management. In the design phase, it is necessary to consider statutory and building regulations, standards, technical specifications, design and construction programmes, health and safety requirements, risk assessment, and environmental requirements [2,25,28]. Table 1 lists the asset operational performance value drivers (VOP) that are needed to optimise the value creation during the design of a green building asset. The project life cycle information is important in value for economic analysis. Over the building's lifetime, the operating cost (running cost) would constitute approximately 80-85% of the total [19,29]. Efforts made in investing in a green building contribute to improve the performance life cycle operations by reducing the energy, water, utility, waste, and operation, and maintenance (O & M) costs [31,32,41]. In addition, a green building maintains a good indoor environment, which offers greater marketability, faster sale, and higher return on investment [33]. Moreover, green buildings have a positive impact on the occupants' health and productivity, which will generate more value for the business. Tenant satisfaction in a building is related to the building's temperature, acoustics, general health, and productivity factors [20]. Table 1 lists the environmental performance value drivers (VEN) that need to be optimized during the design of a green building asset. A green building design must relate to the site's microclimate and the building's functionality should also be adaptable to accommodate future uses to achieve a range of wider social and economic benefits [24]. The green building objectives should include sustainable site development, water efficiency, energy efficiency, indoor environmental quality, and resource consumption of building materials [30]. A green building should also contribute to the value for businesses by reducing the operating costs, offering a longer life cycle and lower development costs, and might improve the occupant productivity. Presently, there are several environmental assessment tools (such as BREEAM and LEED) that provide valuable information that needs to be considered in the green building design process [53,54]. A building that is certified by an environmental organization will obtain many benefits, such as an increase in its market value and lower energy consumption [31,32]. Table 1 lists the management performance value drivers (VMA) that need to be optimized during the design of a green building asset in order to create high value-creating project management activities. The value engineering techniques are sometimes considered as management tools to deliver the project on time with low cost and high quality. Additional value can be unlocked by integrating services, such as commissioning strategy, procurement path, and planning the construction processes [43]. Connaughton and Green [2] mentioned that the value management strategy can be used to identify the project objectives and provide a foundation for the stakeholders making accountable decisions. The NAO [24] stated that "the project teams should communicate well with all stakeholders. They should involve users, contractors and other members of the supply chain at appropriate times throughout the design and construction of the project to benefit from their expertise".  Figure 1 presents the framework showing the research methodology that was adopted in this research. Initially, five primary VCDs were identified through literature and expert judgment. Ninety-eight (98) PVDs were identified to assess the performance of the five identified VCDs. A questionnaire was developed to obtain the opinion of experts in the field on the importance of the identified PVDs. Subsequently, the selected PVDs were ranked by conducting a hand-delivered questionnaire survey, followed by detailed statistical analysis of the collected data by using Statistical Package for Social Sciences (SPSS) and Microsoft Excel software. Finally, the PVDs with the highest importance were ranked and grouped into clusters to facilitate the shareholders and designers to enhance performance efficiency and obtain more value from investment in green building assets. Details of all the steps are provided in the following sub-sections. strategy can be used to identify the project objectives and provide a foundation for the stakeholders making accountable decisions. The NAO [24] stated that "the project teams should communicate well with all stakeholders. They should involve users, contractors and other members of the supply chain at appropriate times throughout the design and construction of the project to benefit from their expertise". Figure 1 presents the framework showing the research methodology that was adopted in this research. Initially, five primary VCDs were identified through literature and expert judgment. Ninety-eight (98) PVDs were identified to assess the performance of the five identified VCDs. A questionnaire was developed to obtain the opinion of experts in the field on the importance of the identified PVDs. Subsequently, the selected PVDs were ranked by conducting a hand-delivered questionnaire survey, followed by detailed statistical analysis of the collected data by using Statistical Package for Social Sciences (SPSS) and Microsoft Excel software. Finally, the PVDs with the highest importance were ranked and grouped into clusters to facilitate the shareholders and designers to enhance performance efficiency and obtain more value from investment in green building assets. Details of all the steps are provided in the following sub-sections.   parts: (i) general information of the respondent, e.g., organization name, email address, phone number, postal address, job title, level of experience, etc., and (ii) the opinion of respondent on the value drivers.

Framework for Development of Value Creation Drivers
The respondents were categorised into three groups based on the information obtained from first part of the questionnaire, including managers, engineers, and architects, to perform rational statistical analysis. In part two of the questionnaire, a Likert scale ranging from 1 to 5 was used to rank the VCD, with 1 being 'not important', 2 being 'slightly important', 3 being 'moderately important', 4 being 'very important', and 5 being 'extremely important'. Table 2 presents a part of the sample questionnaire survey form. The sample sizes used in this study were selected based on the professionals with knowledge of value engineering applications in the Saudi Arabian construction industry and the survey was conducted during the period from 19 December 2014 to 31 January 2015. The sample size is usually selected from a group of individuals to represent specific aspects of an identified population [55]. As per SAVE International, more than 1350 people have obtained value engineering certificates in Saudi Arabia [22,56]. Of this number, approximately 30 of them have obtained a Certified Value Specialist (CVS) certification. It was found that around 16% are from Saudi Arabia, as of year 2015, when compared with the total number of certified personal worldwide (i.e., about 8838). For a confidence interval of 10% and confidence level of 95% from the population of 1356, the research needed at least 76 respondents. Expecting a large number of non-respondents, the questionnaires were delivered to a sample size of 300 professionals in the Saudi Arabian construction industry. Precisely, the following methodology was used to develop the questionnaire: Step 1: Preparation of draft questionnaire.
Step 2: Review of draft questionnaire by experts, e.g., question format, context, and relevance.
Step 3: Revision of draft questionnaire.
Step 4: Pre-test the revised questionnaire by the experts from academia and profession.
Step 5: Update the revised questionnaire based on the 2nd feedback from experts.
Step 6: Final check for the need of a subsequent pre-test.
Step 7: IF 'another pre-test is required' start from Step 4 again, OR 'finalize the questionnaire'.

Questionnaire Validation
A pilot study was conducted to verify the clarity and readability of the designed data collection format before sending the questionnaire to the chosen sample. The questionnaire validation was performed through deliberation and review of the questionnaire design before it was sent to the Sustainability 2019, 11, 5867 9 of 33 potential respondents by conducting a final check by the academicians and professionals who were selected based on their experience in the field and research interests. Finally, the questionnaire was updated based on their recommendations and comments.

Questionnaire Delivery and Survey Response
The identified value drivers were examined by sending a questionnaire to members of the Saudi Arabian construction industry to find out the most significant ones for use in the development of the green building design in the country. The hand-delivery questionnaire method was preferred. as it has a higher response rate and cheaper costs than a typical mail survey [55,57].
The questionnaire was hand-delivered to 300 professionals, and 89 of them returned their fully completed questionnaire. According to Akintoye [58], the normal response rate for a postal questionnaire survey in the construction industry is 20-30%. Hence, the response rate of 29.7% in present study was an acceptable rate of response for the selected sample size.

Descriptive Statistics and Data Ranking
Statistical Package for the Social Sciences (SPSS) and Microsoft Excel were used to analyse the responses on VCDs. The comparison of the data ranking was carried out while using severity indices, average weighted mean, and standard deviation of each value creation driver. Further analyses of the data ranking were based on respondents' answers and their experience (0-5 years, 6-10 years, and more than 10 years of experience) and their professional job (manager, engineer, or architect).
The means, standard deviation, and the coefficient of variation values, which were calculated using Microsoft Excel, were found to be in agreement for all three groups of respondents (managers, engineers, and architects).
A mean weighted rating for each PVD was computed to indicate the importance of each indicator, while using Equation (1). Meanwhile, the range varies from 1 to 5; therefore, the moderate point for performance value drivers is 3.
where R is the rating of each performance value driver (1, 2, 3, 4, 5), F is the frequency of responses, and n is the total number of responses. A severity index (S.I.) measure was employed in order to rank the VCD according to their significance in terms of the percentage (%), as: where W is the weight of each rating (1/5, 2/5, 3/5, 4/5, 5/5).

Testing the Hypotheses
Analysis of variance (ANOVA) was conducted to justify the statistical differences between the groups' responses. The SPSS software was used with a significance level of 0.05 to examine the differences between the groups regarding the importance of the PVDs. The following hypothesis were assumed: • H 0 : p > 0.05: There is no significant difference among the respondents' ratings for the importance of the PVDs. • H 1 : p < 0.05: There is significant difference among the respondents' ratings for the importance of the PVDs (at least one of the groups is significantly different from other groups).
Subsequently, a follow-up test was performed to make multiple comparisons to identify any significant difference among the respondents. The follow-up test used in this research was the Post Hoc Multiple Comparison Test; the Tukey test was used for the purpose, as the sample size is uneven.

Factor Analysis and Data Reduction
The objective of using a factor analysis process is to reduce data and eliminate redundant data that are not highly correlated variables from the survey. Factor analysis is often used to reduce the data and identify a small number of components, which shows the observed variance in a much larger number of manifest variables (SPSS 22.0.0.1). As a large number of variables often make the data more difficult to understand and manage, factor analysis allows for the researcher to reduce the number of factors without losing too much information from the original variables provided [59,60].
In the factor analysis process, a matrix of correlation coefficients and the components that have an Eigenvalue of 1 were extracted. Finally, a rotated component matrix was generated to find out which PVDs have a more effective influence on each component. The identified 98 PVDs were reduced down to 51 PVDs through identifying redundant data. The factor analysis process that is presented in Figure 2 shows that all the PVDs within each value creation drivers have been categorized into different clusters through the use of data reduction in SPSS. In present research, each of the VCDs contain two clusters which are explained in the following sections.
were assumed:  H0: p > 0.05: There is no significant difference among the respondents' ratings for the importance of the PVDs.  H1: p < 0.05: There is significant difference among the respondents' ratings for the importance of the PVDs (at least one of the groups is significantly different from other groups).
Subsequently, a follow-up test was performed to make multiple comparisons to identify any significant difference among the respondents. The follow-up test used in this research was the Post Hoc Multiple Comparison Test; the Tukey test was used for the purpose, as the sample size is uneven.

Factor Analysis and Data Reduction
The objective of using a factor analysis process is to reduce data and eliminate redundant data that are not highly correlated variables from the survey. Factor analysis is often used to reduce the data and identify a small number of components, which shows the observed variance in a much larger number of manifest variables (SPSS 22.0.0.1). As a large number of variables often make the data more difficult to understand and manage, factor analysis allows for the researcher to reduce the number of factors without losing too much information from the original variables provided [59,60].
In the factor analysis process, a matrix of correlation coefficients and the components that have an Eigenvalue of 1 were extracted. Finally, a rotated component matrix was generated to find out which PVDs have a more effective influence on each component. The identified 98 PVDs were reduced down to 51 PVDs through identifying redundant data. The factor analysis process that is presented in Figure 2 shows that all the PVDs within each value creation drivers have been categorized into different clusters through the use of data reduction in SPSS. In present research, each of the VCDs contain two clusters which are explained in the following sections.

Distribution of the Respondents
The questionnaire consisted of 98 identified PVDs that were distributed in five VCDs, as follows: financial performance (20 drivers), functional performance (34 drivers), operational performance (16 drivers), environmental performance (16 drivers), and management performance (12 drivers). The PVDs were ranked within the job description, the expert experience, and on overall basis.
Eighty-nine (89) respondents working in Saudi Arabia were asked two questions about their job description and experience to provide study and statistical data analysis. 40 respondents (45%) were managers, 34 respondents (38%) were Engineers, and 15 respondents (17%) were Architects.
Their years of work experience were: 14 (16%) had 0 to 5 year experience, 25 (28%) had 6 to 10 year experience, and 50 (56%) had more than 10 years of experience. Table 3 shows the respondents' years of experience. These statistics provide justification for the relevance and significance of their responses and reasonable support for the arguments in this research.  Table 4 illustrates the output of the ANOVA analysis for each value attribute. The table shows that some statistically significant differences do exist between the groups of respondents' responses for some of the PVDs, such as for VFI2, VFU2, VFU13, VFU26, VFU28, VOP1, VOP6, VOP9, VOP11, VEN4, VEN5, VEN12, VMA1, VMA3, and VMA5. It can be seen in the table that the p values for these PVDs are less than 0.05. It was also observed that, for the drivers that were significantly different, the F values were equal to or larger than the F critical value of 3.10. The ANOVA results presented in Table 4 do not show specific means for which groups are different from other ones. Therefore, a follow-up Post Hoc Multiple Comparison Test was performed to provide multiple comparisons. The Tukey test, as the post hoc tests, was used due to uneven sample size in present research. The PVDs with higher F values, as illustrated in Appendix A, describe the groups in different subsets with significant difference. For example, the rating for VFI2 is not significantly different between Managers and Architects, but it is found to be significantly different between Engineers and Managers or Architects.

Factor Analysis and Data Reduction
Based on the factor analysis and data reduction, the most effective PVDs for value creation are 10 financial performance drivers that are distributed into two clusters (OPEX and CAPEX); 18 functional PVDs distributed into two clusters (Longevity, Reliability); nine PVDs for assessing the operational performance distributed into two clusters (Manageability, Energy, and Efficiency); eight environmental PVDs distributed into two clusters (Eco-resources, Adaptability); and, six drivers distributed into two clusters (Control, Planning) for assessing the management performance of a green building. Table 5 shows the components that were extracted by principle component analysis (PCA). It can be seen in the table that the components were set according to a series of correlations between different financial PVDs. The first column shows the components and the next three columns are categorised as: initial Eigenvalues, which are related to the Eigenvalue of the correlation matrix and indicate which components can remain in the analysis. Factor analysis was considered for the components with Eigenvalues of more than one, whilst those with Eigenvalues of less than 1 were excluded [59,60].

Financial Performance
The next category, Extraction Sum of Squared Loadings, shows the sum of the squared loadings for the un-rotated PVDs, and the last category, Rotation Sums of Squared Loadings, is for the rotated PVDs' solution. The initial Eigenvalues and rotated were used to confirm the variation that was explained by each extracted value creation component.
In this analysis of the importance of the financial PVDs, just six components carry an Eigenvalue of more than 1 and account for nearly 67.6% of the variance, as shown in the Cumulative % column. Consequently, these six components can be considered to be representative of all the 20 financial PVDs included in this study.
Matching Table 5, the PCA shows that six components with a Eigenvalue of more than 1 are selected. Therefore, the following phase is the extraction of a rotated component matrix in order to find out which financial PVDs are having the highest level of influence on project value creation. Table 6 illustrates this level of influence, where the matrix loading scores are presented. The degree of influence of each value attribute for all the financial PVDs can be seen by using varimax rotation, and the PVD with the highest rate of influence can be distinguished. It is suggested that drivers' loadings with an absolute value greater than 0.4 should be interpreted whilst ignoring the + ve or − ve sign, which explains around 16% of the variance in the variable [60,61].
The drivers with the highest scores and correlation values in Table 6 were chosen for each component. For example, the value attribute VFI1 (0.695) has greater influence on component 3 s compared to other components, whereas the driver VFI11 (0.528) has more influence on component 1 in relation to other components, and VFI2 (0.876) has more influence on component 6 in relation to other components. This method is used for all of the drivers and components to extract the most important PVDs for each component.
After applying factor analysis and data reduction to the financial PVDs, the questionnaire's 20 drivers were reduced to six components, which are shown in Table 7. The table shows the percentages of variance of each component, Eigenvalue, loading score, and the value attribute, which are extracted from Tables 5 and 6.
The two new clusters that are presented in Table 8 are formed based on the six extracted components and their most important value drivers. The new clusters are considered to comprise the relevant financial performance design indicators for assessing the value created by green building design. The percentage of variance of each cluster is extracted from Table 7 by calculating the percentage of variance of each component in the generated clusters.
The variance percentage of each attribute is extracted from Table 7, and the percentage of variance of each cluster is calculated by a summation of each component's variance in the same generated cluster (see Table 8). For example, the OPEX cluster in Table 8 is one of two clusters for financial performance design indicators; it is composed of component 1 (variance of 15.92%), presenting VFI11 and VFI15 as the main indicators of its group, and component 6 (variance of 7.303%), presenting VFI2 and VFI5 as the main indicators of its set. Consequently, the percentage of variance for this cluster (OPEX) in Table 8 is calculated by the summation of the percentage of variance of its components. Therefore, the percentage of variance for the OPEX cluster is computed as 15.92% + 7.3% = 23.22%.
The financial performance design indicators are grouped into two clusters, which are highly manageable without losing a lot of data, and just 100% − 67.6% = 32.4% of the existing information is compromised. While using the method of factor analysis and data reduction, the questionnaire's 20 PVDs are reduced to 10 and then grouped into two fundamental clusters. Table 8 presents the final results of factor analysis and data reduction for the financial performance drivers.

Functional Performance
A similar process was carried out for the functional performance. For this VCD, just seven components carry Eigenvalues of more than 1 and account for nearly 71.2% of the whole variance. Consequently, these seven components can be considered as being representative of the 34 PVDs that were included in this study.
The functional PVDs were grouped into two clusters, which are highly manageable without losing a large amount of data, and therefore just 100% − 71.3% = 28.7% of the existing information is compromised. Using the method of factor analysis and data reduction, the questionnaire's 34 drivers were reduced to seven components, and then grouped into two fundamental clusters, which finally include 18 most significant PVDs. Table 9 presents the final results of factor analysis and data reduction for the functional performance drivers.

Operational Performance
PCA revealed that three components were extracted that carry Eigenvalues of more than 1 and account for 68.661% of the whole variance. The operational performance is categorised into two clusters, which are highly manageable without losing lots of data, and, therefore, just 100%− 68.661% = 31.34% of the existing information is compromised. While using the method of factor analysis and data reduction, the questionnaire's 16 drivers were reduced to three components and then grouped into two pivotal clusters, including nine most significant PVDs. Table 10 presents the final results of factor analysis and data reduction for the operational performance.

Environmental Performance
The data reduction process is looking for variables that correlate highly with a set of other variables. For environmental performance assessment, four components found with an Eigenvalue larger than 1 accounting 74.5% of the whole variance were selected for further analysis. Table 11 shows the two new clusters comprising the relevant environmental performance design indicators for assessing the value that is created by green building design. The percentage of variance of each cluster is calculated by summation of each component's variance in the same generated cluster. The eco-resources cluster has a variance of 37.425% and Adaptability has a variance of 37.05%. The environmental PVDs are categorised into two clusters, which are highly manageable without losing a large amount of data and, consequently, just 100% − 74.475% = 25.525% of the existing information is compromised. The questionnaire's 16 drivers were reduced to four components and then grouped into two fundamental clusters with half of the original PVDs that represent the most relevant data on environmental performance design indicators for value creation using the method of factor analysis and data reduction.

Management Performance
The extracted components in this VCD have a cumulative variance of 66.568% for the first two components, which will be taken into account as being representative of the whole drivers. The Eigenvalue for component 1 is 6.936 and for component 2 it is 1.053 and so these two components were selected for further analysis. Table 12 groups the management PVDs into two clusters, which are highly manageable without losing a large amount of data and, therefore, just 100% − 66.568% = 33.4% of the existing information is compromised. Using the method of factor analysis and data reduction, the questionnaire's 12 PVDs are reduced to six grouped under two pivotal clusters.   Provide cost control to achieve the project objectives

Financial Performance Value Drivers
The analysis showed that the survey respondents thought that the financial performance value drivers that are shown in Figure 3a,b significantly contribute to value creation in green buildings. The mean score of these drivers ranges between 3.92 and 4.16 (2.5 is the mean of the scoring scale). Figure 3 shows that the respondents have very diverse views regarding the importance of the financial drivers. It appears that engineers considered them to be least important, whereas managers were clued up to the significance of optimizing financial parameters. For instance, VFI11, VFI15, and VFI9 (Maximize residual value, Optimize risk-return ratio of alternative options, Return on investment) are ranked as the least important financial drivers. More unexpectedly, value drivers VFI3 and VFI4 (Maximize the cost efficiency to build and deliver/achieve cost certainty) did not receive the highest score. According to the Green Building Council [31], these two factors are necessary for optimizing the upfront cost with a view to decrease the long-term life cycle costs through "green buildings that feature high-performance façades and energy-efficient building systems".

Financial Performance Value Drivers
The analysis showed that the survey respondents thought that the financial performance value drivers that are shown in Figure 3a,b significantly contribute to value creation in green buildings. The mean score of these drivers ranges between 3.92 and 4.16 (2.5 is the mean of the scoring scale). Figure 3 shows that the respondents have very diverse views regarding the importance of the financial drivers. It appears that engineers considered them to be least important, whereas managers were clued up to the significance of optimizing financial parameters. For instance, VFI11, VFI15, and VFI9 (Maximize residual value, Optimize risk-return ratio of alternative options, Return on investment) are ranked as the least important financial drivers. More unexpectedly, value drivers VFI3 and VFI4 (Maximize the cost efficiency to build and deliver/achieve cost certainty) did not receive the highest score. According to the Green Building Council [31], these two factors are necessary for optimizing the upfront cost with a view to decrease the long-term life cycle costs through "green buildings that feature high-performance façades and energy-efficient building systems".  Table 13 shows the research question related to the financial drivers cluster and hypothesis test. The ANOVA test shows that there were significant differences between the respondents regarding the VF12 financial performance driver. The possible reasons for these differences can be attributed to the fact that the respondents have different perspectives on how to implement OPEX strategy for value creation purposes. The surprising aspect of the results is that the architects did not highly rank this value driver. This might suggest that they were not aware of the importance of value creation through the optimization of operation costs.  Table 13 shows the research question related to the financial drivers cluster and hypothesis test. The ANOVA test shows that there were significant differences between the respondents regarding the VF12 financial performance driver. The possible reasons for these differences can be attributed to the fact that the respondents have different perspectives on how to implement OPEX strategy for value creation purposes. The surprising aspect of the results is that the architects did not highly rank this value driver. This might suggest that they were not aware of the importance of value creation through the optimization of operation costs.

Results
The ANOVA results indicated that: There were significant differences between the survey participants regarding value drivers: VFI2: Efficiency of operational expenditure (OPEX)

Comments
Reducing operational expenditure is essential for reducing maintenance, water, energy, etc., expenses. The importance of each financial value driver is assessed according to professional bias. The results here might be influenced by that fact that Kingdome of Saudi Arabia (KSA) respondents are not familiar with the private sector expectation of a better return from the investment in green buildings. The author believes that "achieving the optimum balance between capital costs, a building's operating and maintenance costs and residual whole-life value" (NAO) is necessary for value creation.

Conclusion
The null hypothesis was rejected for VFI2: Efficiency of operational expenditure. The null hypothesis (H 0 : p > 0.05) was retained for the other financial value drivers.

Functional Performance Value Drivers
Building functionality is considered as "functionality-the arrangement, quality and interrelationship of spaces and how the building is designed to be useful to all" [24]. One paradigm behind the design of green buildings is the focus on selecting material and design solutions based on durability/reliability and longevity performance criteria. The idea behind this design paradigm is that reliability and longevity increase the life service span of the building's systems, which results in fewer maintenance cycles and cleaning requirements, leading to financial value benefits. Figure 4a,b indicate that engineers did not highly rate drivers VFU27 and VFU28 (ensure substructure functional requirements meet a satisfactory level of performance and ensure superstructure functional requirements meet a satisfactory level of performance). This is a surprising result, because the maintenance of substructure and superstructure is normally costly and it leads to disruption of the building operation, which results in further additional revenue losses.
Reliability in this study concerns the potential of a green building to be reliable for users while also providing comfort. VFU1 "Maintain adaptable building-useful to all" is considered a key driver for delivering value to the businesses of the green building's occupants, as articulated by the NAO [24]: the "building [needs to] be easily adaptable to meet the future needs of users including expansion and change of use". This study's results show that there is an unspoken agreement between the respondents on the effectiveness of VFU22 (ensure designed elements are standardized) in value creation. Engineers and managers also agree on the usefulness of VFU20 (suitability and maintainability of materials) in the value engineering analysis. However, the architects perceived that this driver is not very useful. Sustainability 2019, 11, x FOR PEER REVIEW 21 of 31

Operational Performance Value Drivers
Operation performance value drivers are associated with issues concerned with managing, maintaining, operating, and cleaning the green facility once it is in operation. The present study clustered the operation performance value creation drivers into "Manageability" and "Energy and efficiency" drivers. According to the NAO [24], the manageability drivers have a significant impact on value creation. It is well known that there is a huge cost burden that is associated with acquiring, operating, maintaining, and disposing of a building and its complements. Thus, specifying building functions and building systems that are "Easy to maintain" (VOP6) is considered to be a vital value creator. In this survey, the architects recognized the importance of this driver, but the managers seemed to be unaware of its significance. Although Chiras [51] emphasises the importance of making buildings easy to operate, service, and maintain, the engineers in this survey perceived VOP11 "Provide building systems that are easy to operate and control" to be less important that other values in the cluster. VOP8 "Easy to operate" is considered by [24] and [51] to be a significant value generator. For example, the NAO state that "day to day, the building should be easy to clean, maintain and operate due to its finishes, layout, and structure and engineering systems". The results also demonstrated that managers alleged that VFU16 (Maintain durability) might not be useful in creating value. Engineers did not rank the VFU18 "Create reliable building-safer" value driver highly. More surprisingly, engineers and architects both thought VFU1 "Maintain adaptable building-useful to all" is not a very beneficial value driver as compared to others. The respondents' views of these drivers are not in keeping with existing literature, most of which point to the fact that this value driver should be an essential part of green building design. A plausible explanation for this is that Kingdome of Saudi Arabia (KSA) professionals may not be aware of recent studies that demonstrate the tangible and intangible benefits of green buildings. Table 14 shows the research question regarding the functional drivers cluster and hypothesis test. The ANOVA test shows that there were significant differences between the respondents regarding several functional PVDs.

Results
The ANOVA results indicated that: There were significant differences between the survey participants regarding value drivers: VFU2: Increase life of services VFU13: Provide disability access VFU26: Provide functional ability of the foundations requirements (strength and stability) VFU27: Ensure substructure functional requirements meet a satisfactory level of performance VFU28: Ensure superstructure functional requirements meet a satisfactory level of performance

Researcher's observation
It is understandable that respondents disagreed on structural functionality as a value-generating driver. However, there is unspoken agreement in the literature that increasing the life of services is an essential value-generating driver.

Conclusion
The null hypothesis was rejected for VFU2, VFU13, VFU26, VFU27, and VFU28 value drivers. The null hypothesis (H 0 : p > 0.05) was retained for other functional performance value drivers.

Operational Performance Value Drivers
Operation performance value drivers are associated with issues concerned with managing, maintaining, operating, and cleaning the green facility once it is in operation. The present study clustered the operation performance value creation drivers into "Manageability" and "Energy and efficiency" drivers. According to the NAO [24], the manageability drivers have a significant impact on value creation. It is well known that there is a huge cost burden that is associated with acquiring, operating, maintaining, and disposing of a building and its complements. Thus, specifying building functions and building systems that are "Easy to maintain" (VOP6) is considered to be a vital value creator. In this survey, the architects recognized the importance of this driver, but the managers seemed to be unaware of its significance. Although Chiras [51] emphasises the importance of making buildings easy to operate, service, and maintain, the engineers in this survey perceived VOP11 "Provide building systems that are easy to operate and control" to be less important that other values in the cluster. VOP8 "Easy to operate" is considered by [24] and [51] to be a significant value generator. For example, the NAO state that "day to day, the building should be easy to clean, maintain and operate due to its finishes, layout, and structure and engineering systems".
As shown in Figure 5a,b, both architects and engineers recognized the importance of this value driver. Although, Chiras [51] pointed to the importance of "VOP11" (Provide building systems that are easy to operate and control", the engineers in the survey perceived that this value driver is less important. However, there does appear to be a general agreement between the respondents that VOP15 "Reduce operational risk" is an important value driver. In the energy and efficacy value drivers cluster the respondents were in agreement regarding the effectiveness of VOP1 "Reduce/minimize/save energy usage" and VOP2 "Maintain efficiency in terms of energy" value generators. This result denotes that these two drivers are important. This finding is supported by current literature. The importance degree scores for VOP3 "Increase efficiency of utilities" and VOP4 "Increase efficiency of heating, cooling and lighting" value drivers range between 3.97 and 4.40, with architects viewing these two drivers as being less significant in value generation. Table 15 shows the research question about operational drivers cluster and hypothesis test. The ANOVA test shows that there were significant differences between the respondents regarding several operational performance drivers. Sustainability 2019, 11, x FOR PEER REVIEW 23 of 31

Environmental Performance Value Drivers
The tangible and intangible environmental values that were provided by green buildings have been widely reported. These include waste reduction, and economic and social benefits. These benefits are now well established and the next quest revolves around "how green buildings deliver on economic priorities, such as return on investment and risk mitigation, and on social priorities, such as employee productivity and health" [31]. The NAO [24] stressed that buildings must include the principles of environmental sustainability in their design and operation, and use renewable materials. The environmental performance value drivers in this study were grouped into 'Eco-resources" and "Adaptability". The findings showed that VEN3 "Provide indoor environmental quality" is considered to be less important by architects than by engineers and managers. This is somehow unexpected, as architects are normally responsible for specifying the indoor environmental parameters for interior and exterior design of the buildings. Existing literature indicates that VEN4 "Access to natural light, management of air quality and temperature" and VEN5 "Increase use of natural ventilation" drivers are necessary for a health working environment and increasing productivity. Loftness [61] points out that "improved temperature control, air quality, lighting control, and access to the natural environment will result in measurable productivity, health [benefits] . . . ".
In particular, architects emphasized natural ventilation as a key driver for value generation. The importance of VEN7 "Specifying low-maintenance, durable, environmentally preferable materials and equipment" and VEN10 "Minimize consumption of resources" is owing to the fact that low-maintenance building material and components result in longer service life, which results in economic (lower cost of maintenance) and environmental (lower waste and emissions from material disposal) benefits. Figure 6a,b indicate that architects and managers are not entirely convinced that the inclusion of VEN10 value engineering analysis aids the quest for value creation in green buildings. plans to achieve the project objectives", and VMA10 "Able to construct to scope/cost/budget/schedule/quality" as highly as engineers and architects did. Table 17 shows the research question about the management drivers, clusters, and hypothesis test. The ANOVA test shows that there were significant differences between the respondents regarding several management performance drivers.   The reusability/adaptability value drivers aim to promote value creation through adaptation to local conditions and the reuse of resources to minimize waste and optimise cost. The VEN11 "Conserve water resources" value driver is seen as important, but it is only ranked 14th, whereas it would be expected to be ranked among top ten value drivers in the KSA environment where water comes mainly from desalination. Figure 6b shows that engineers rate VEN12 "Respond to site microclimate" more highly than the architects do, although architects would be expected to highly rank this value driver. The figure also suggests that engineers and managers were not very concerned about the issue of VEN8 "Maximize resource reuse". Evidence from existing literature suggests that these three value drivers are of importance in value creation [62,63]. Table 16 shows the research question regarding the environmental drivers cluster and hypothesis test. The ANOVA test shows that there were significant differences between the respondents regarding several environmental performance drivers.

Management Performance Value Drivers
The drivers considered in this section are related to the management processes used, and the selection of an integrated team working throughout the development of the green building supply chain. There are opportunities to maximize the value and minimize the waste at every stage of the construction and procurement process, from the minute that the need for a building is identified to when it is ready for use [24]. Effective management of the development and operation processes of green buildings is crucial in value creation. This entails close collaboration and communication between all the stakeholders, and requires appropriate objectives (relating to costs, emissions reduction, etc.) to be developed right at the beginning of the development process and monitored throughout the service life of the green building. Figure 7 shows the order of ranking of the most important management performance value drivers in this study. All of the management value drivers are ranked below 50. It appears that engineers and managers highly ranked VMA11 "Completed to specification", whilst the managers did not rank VMA5 "Provide cost control to achieve the project objectives", VMA6 "Produce effective plans to achieve the project objectives", and VMA10 "Able to construct to scope/cost/budget/schedule/quality" as highly as engineers and architects did.
The methodology that was developed in present study can facilitate the managers, engineers, planners, and architects to assess the value added during design of green buildings. Although the methodology has been applied to the scenarios of green buildings in Saudi Arabia, it can be used for other regions around the world. However, the outcomes in terms of ranking of the drivers might be different depending on the identified PVDs and VCDs, number and experience of respondents, and geographical location of the study area.

Results
The (ANOVA) results indicated that: There were significant differences between the survey participants regarding value drivers: VMA1: Provide effective project management and delivery VMA3: Create strategic planning VMA5: Provide cost control to achieve the project objectives Researcher's observation Effective management by an integrated project team is essential to achieving this value. Effective value planning through the development of a project execution plan and the organisation of the project team is required to create added value to the project.

Conclusion
The null hypothesis were rejected for VMA1, VMA3 and VMA5 value drivers. The null hypothesis (H0: p > 0.05) was retained for other management performance value drivers  Table 17 shows the research question about the management drivers, clusters, and hypothesis test. The ANOVA test shows that there were significant differences between the respondents regarding several management performance drivers.

Results
The (ANOVA) results indicated that: There were significant differences between the survey participants regarding value drivers: VMA1: Provide effective project management and delivery VMA3: Create strategic planning VMA5: Provide cost control to achieve the project objectives

Researcher's observation
Effective management by an integrated project team is essential to achieving this value. Effective value planning through the development of a project execution plan and the organisation of the project team is required to create added value to the project.

Conclusion
The null hypothesis were rejected for VMA1, VMA3 and VMA5 value drivers. The null hypothesis (H 0 : p > 0.05) was retained for other management performance value drivers The methodology that was developed in present study can facilitate the managers, engineers, planners, and architects to assess the value added during design of green buildings. Although the methodology has been applied to the scenarios of green buildings in Saudi Arabia, it can be used for other regions around the world. However, the outcomes in terms of ranking of the drivers might be different depending on the identified PVDs and VCDs, number and experience of respondents, and geographical location of the study area.

Conclusions and Recommendations
This research aimed to assess the sustainability of green buildings in Saudi Arabia. Green buildings may contain higher levels of complexities in their designs and operations in comparison to the conventional buildings. Hence, investments need to be evaluated with the involvement of multiple stakeholders, such as consultants, contractors, general public, governmental institutions, etc. With such diverse decision-makers, selecting suitable value creation drivers is a difficult task. In addition, their varying perceptions and experiences also impact the selection process of the drivers.
The decision-making framework proposed in present research provides a systematic approach for identifying, selecting, and ranking a set of the most important value creation drivers (VCDs) for green buildings. Five top-level VCDs covers the financial, functional, operational, environmental, and management aspects of the green buildings. Ninety-eight (98) performance value drivers (PVDs) that were identified through literature were evaluated through questionnaire surveys and subsequent statistical analysis. The response rate of 29.7% was achieved from 89 respondents out of 300 professionals working in the construction industry of Saudi Arabia. Further, 50% of these respondents have more than 10 years of relevant experience.
Each VCD was further sub-divided into two clusters (i.e., total 10 clusters for 5 VCDs) for the effective use of 51 most important PVDs. The proposed methodology provides a basis for improving the performance efficiency and value from investment for green buildings in Saudi Arabia, Arab Gulf countries, and elsewhere.
Further work needs to be carried out to refine the extracted value drivers for different types of the buildings for developing a more robust tool to assess value addition in green buildings. Moreover, future work should verify the correlation between risk factors and the value creation drivers.
Author Contributions: W.A. developed the methodology, developed questionnaire survey, performed detailed data collection and analysis, and was involved in paper writing. H.H. and H.B. were involved in the development of methodology, and paper writing.  Figure A1. Historical Post Hoc test for rating the importance of the value attributes.