Abstract
Purpose
The conventional project delivery (CPD) approach has been reported in the literature as the most widely used project delivery method in the construction industry globally compared to other delivery methods. However, researchers and practitioners have argued that the approach, specifically during the production of design documentation under the CPD, lacks certain capabilities that ensure quality and enhance project delivery. Therefore, this study aims to use the Ghanaian construction industry to identify the capabilities required of the CPD in practice, particularly during the production of design documentation.
Design/methodology/approach
The study design follows a pragmatist philosophy and uses mixed methods based on a deductive approach. Data collection involved a questionnaire survey, followed by semi-structured interviews. Quantitative data analysis used descriptive and inferential statistics, whereas qualitative data analysis used content analysis with the assistance of IBM SPSS and QSR Nvivo 12 Pro.
Findings
Findings indicate that there should be incentives for producing good design documentation quality; mandatory coordination of design documentation; improving collaboration among designers; and allowing contractors to make input during the design stage.
Practical implications
The results indicate the need for the identified capabilities to be introduced in the CPD approach to improve design documentation quality.
Originality/value
This study offers a significant insight into the specific capabilities that are required of the CPD approach in practice particularly, in the production of design documentation
Keywords
Citation
Agbaxode, P.D.K., Saghatforoush, E. and Dlamini, S. (2023), "Capabilities required of the conventional project delivery (CPD) approach in producing quality design documentation: the Ghanaian construction industry perspective", Journal of Engineering, Design and Technology, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JEDT-11-2022-0559
Publisher
:Emerald Publishing Limited
Copyright © 2023, Peter Dodzi Kwasi Agbaxode, Ehsan Saghatforoush and Sitsabo Dlamini.
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
There are numerous project delivery methods in use in the construction industry globally. However, the conventional project delivery (CPD) approach is the most widely used method across the world (Agbaxode et al., 2021b; Harper et al., 2016; Mesa et al., 2016; Nawi et al., 2014). In this approach, a general contractor undertakes the construction, whereas the design and supervision are carried out by specialist consultants (Masterman, 2003). According to Ebrahimi and Dowlatabadi (2019), the CPD approach engages project professionals and firms at different phases of the project, working in isolation and independently of each other, focusing solely on their respective portions of work (Durdyev et al., 2019). However, according to Viana et al. (2020), excessive reliance on multiple cultures by these professionals and firms working in isolation leads to the fragmentation of CPD. This fragmentation is also attributed to a lack of collaboration or integration (Durdyev et al., 2019). These limitations of the CPD approach often result in poor design documentation quality (Agbaxode et al., 2021a, 2021b; Akampurira and Windapo, 2019), leading to low productivity, schedule delays, cost overruns and poor-quality project delivery (Zhang et al., 2018). These findings are consistent with an earlier study by Tilley et al. (2002), which indicated a lack of collaboration among project team members, particularly during the design documentation stage, to ensure consistency within the documents produced.
This notwithstanding, the CPD approach remains the dominant and widely used method in the industry globally (Aldossari et al., 2021; Salla, 2020; CMAA, 2012). Therefore, there is a need for changes and initiatives to address the limitations of the CPD approach in producing quality design documentation (Agbaxode et al., 2021b; Akampurira and Windapo, 2019). This requires certain capabilities, such as the early involvement of consultants in the project, especially during the design stage, to enhance planning and understanding (Ling et al., 2020; Agbaxode et al., 2020; Ma et al., 2018). According to researchers and practitioners, these capabilities will contribute to efforts aimed at improving the quality of design documentation (Agbaxode et al., 2021b; Abdallah et al., 2018; Dosumu and Aigbavboa, 2018; Brown, 2002).
In a broader project management process, there are many factors that can significantly influence project delivery and overall quality. However, this study considers design documentation as one critical aspect of the process and provides the necessary capabilities to enhance quality. Therefore, this study used the Ghanaian construction industry to identify specific capabilities that are required in the CPD approach, particularly during the production of design documentation, to improve quality.
2. Literature review
2.1 Background
Although the construction industry globally has a variety of project delivery methods, the CPD (design-bid-build) approach where the owner hires a consultant to design the project, after which a contractor is procured to execute the works is dominant (Addy et al., 2018; Mesa et al., 2016; Nawi et al., 2014; Fish and Keen, 2012). Comparably, it is the widely used method in the industry globally (Salla, 2020; CMAA, 2012). Its usage in the construction industry in terms of market share is 60% compared to the other existing delivery methods (Aldossari et al., 2021; CMAA, 2012). This resonates with statistics that over 90% of construction projects in Ghana, particularly in the public sector are delivered using this approach (Buertey et al., 2021; Ameyaw and Oteng-Seifah, 2010). Therefore, Addy et al. (2018) posit that the CPD approach will continue to dominate the construction industry due to the industry’s lack of readiness to apply new delivery methods.
Some major project delivery methods in use are design-build (DB); construction management (CM); public–private partnerships (PPPs) and integrated project delivery (IPD) (Ashcraft, 2022; Patterson et al., 2021; CMAA, 2012). The DB is where the owner contracts a single entity (design-build contractor) to be responsible for both the project design and construction. The CM is where the owner hires a construction manager to oversee the entire construction process. The PPPs is where based on the collaboration, the private sector takes on the responsibility to deliver the project while the public sector retains ownership. Other project delivery methods exist but they are variations of these four (Patterson et al., 2021; Aldossari et al., 2021). While CPD accounts for about 60% of usage in the industry globally, CM accounts for 25%, DB accounts for 15% and IPD accounts for less than 1% usage (Aldossari et al., 2021; CMAA, 2012). However, the CPD remains the dominant delivery method in the Ghanaian construction industry (Buertey et al., 2021; Ameyaw and Oteng-Seifah, 2010).
Even though the CPD approach is the most widely used method in the industry globally, it is characterised by wide dissatisfaction (Agbaxode et al., 2021b; Viana et al., 2020). It has been widely criticised for its inability to produce quality design documentation (Shoar and Payan, 2021; Agbaxode et al., 2021b; Zaneldin, 2020; Yap and Skitmore, 2018; Harper et al., 2016; Mesa et al., 2016; Nawi et al., 2014). The CPD approach produces poor design documentation quality as a result of the processes involved (Agbaxode et al., 2021b; Akampurira and Windapo, 2018). This often leads to acrimonious conflicts between various project parties (Rowlinson and McDermott, 2005). It has been argued that this approach lacks collaboration and integration, especially during design, which often results in poor design documentation quality (Shoar and Payan, 2021; Agbaxode et al., 2021b; Zaneldin, 2020; Durdyev et al., 2019; Yap and Skitmore, 2018; Harper et al., 2016; Mesa et al., 2016; Nawi et al., 2014).
2.2 Design documentation quality and capabilities required for the conventional project delivery approach
Poor-quality design documentation is predominant and has become a major concern globally (Akampurira and Windapo, 2019). It has consequences of undesirable high cost of construction projects, project delays and poor quality of completed projects (Shoar and Payan, 2021; Tuhacek and Svoboda, 2019; Abdallah et al., 2018; Akampurira and Windapo, 2018; Mesa et al., 2016). It has been argued that large proportion of defects in construction projects often have roots in the defects of the project documentation (Tuhacek and Svoboda, 2019). Therefore, industry changes and initiatives are required to resolve the problems of design documentation quality (Akampurira and Windapo, 2019) particularly under the CPD. This aligns with Nawi et al. (2014) that process and team integration, especially during the design stage, are significant enablers for change in the industry to achieve success. Some researchers and practitioners recommend the early involvement of consultants in the project especially during design to enhance better planning and understanding (Ling et al., 2020; Agbaxode et al., 2020; Ma et al., 2018). This will enhance design documentation quality when the consultants are encouraged to work as a team rather than as individuals (Maskil-Leitan and Reychav, 2018). Therefore, effective collaboration should be promoted by consultants to ensure better quality project delivery with enhanced efficiency (Pishdad-Bozorgi, 2017). This is why researchers and practitioners have all called for efforts to improve design documentation quality (Agbaxode et al., 2021b; Abdallah et al., 2018; Dosumu and Aigbavboa, 2018; Brown, 2002).
2.3 Efforts towards improving the quality of design documentation
To cure this canker in the industry globally, some developed countries are advancing in the use of integration and technology. However, various strategies that aimed to improve design documentation quality have been proposed. Some proposed the development of an instrument to measure the quality (Akampurira and Windapo, 2019). Others proposed the use of design checklists, departments for quality control (Abdallah et al., 2018; Brown, 2002) and computer programs like Building Information Modelling (BIM) (Dosumu and Aigbavboa, 2018). Some software vendors and practitioners have also promoted the use of BIM as a panacea to design-related errors (Abdallah et al., 2018; Love et al., 2011). However, Pärn et al. (2018), in a study on BIM usage, and Love et al. (2011) on effective BIM usage all argue that BIM alone is not a panacea to poor design documentation quality. This notwithstanding, various tools for evaluating design documentation quality have been developed and used over the years by professionals in the industry [Harputlugil et al., 2014; Giddings et al., 2013; Construction Industry Council (UK), 2002]. Yet, poor-quality design documentation problem still exists (Akampurira and Windapo, 2019). Therefore, the study aims to identify required capabilities to help improve the processes in the CPD approach towards improving design documentation quality using the Ghanaian Construction Industry. It is hypothesised that:
The quality of design documentation will improve when the required capabilities are determined.
3. Methodology
A mixed sequential research design (quantitative followed by qualitative methods) was used, which helped provide relevant and reliable results for this study (Saunders et al., 2019; Creswell and Creswell, 2017; Tashakkori and Teddlie, 2010). As a strategy, a two-tier approach was used, which involved a questionnaire survey (composed of questions adapted from the literature) followed by semi-structured interviews to collect relevant data. The questionnaire survey was conducted online via Google Forms, whereas the interviews were conducted both online and in-person. The study involved clients and professionals such as architects, quantity surveyors, project managers and other relevant professionals in consulting and construction firms in the industry. In analysing the data, IBM SPSS Statistics for Windows, Version 27.0 was used to aid in quantitative data analysis, whereas QSR Nvivo 12 Pro software was used to aid in qualitative data analysis. In determining the quantitative data sample size, the following formula by Saunders et al. (2016, p. 283) was used:
Descriptive and inferential statistics were used for quantitative data analysis, whereas content analysis was used for qualitative data. However, the main quantitative data analysis methods used were the relative importance index (RII), correlation and regression calculations. In determining the significance of each variable, the ranking was based on the RII of each factor (Holt, 2014; Ribeiro and Fernandes, 2010; Zeng et al., 2005). Correlation and regression analyses were carried out in the study to determine the correlation and significant relationships between the variables. In addition, correlation analysis depicts the relationship between the variables and whether or not they relate, whereas regression analysis allowed the determination of the relationship between the dependent and the independent variables (George and Mallery, 2019; Morgan et al., 2019; Aldrich, 2018).
Threats to the validity, reliability and generalisability, which are very important in ensuring the quality of a study (Saunders et al., 2019; Dudwick et al., 2006), were mitigated in this study through the triangulation of data. As a result, reliable information was obtained through questionnaires and semi-structured interviews (Mohajan, 2017). This approach added depth, breadth and richness to the study (Denzin, 2012; Denzin and Lincoln, 2011). In addition, a pilot study was conducted with the questionnaire, and subsequently, Cronbach’s alpha values were calculated. To address potential research biases, multiple data sources were used to corroborate the findings. Informed consent was obtained from respondents and participants, and anonymity and confidentiality of responses were ensured by using numerical codes to represent responses. Furthermore, the research team comprised individuals from diverse backgrounds to mitigate any potential biases stemming from the researchers’ perspectives.
4. Data analysis
Analysis involves data from the questionnaire survey and semi-structured interviews to answer the research question:
What capabilities are required of the CPD approach in producing quality design documentation?
The questionnaire survey recorded 235 responses, and 15 expert participants were interviewed.
4.1 Quantitative data analysis
The main research question for the study is: What capabilities are required of the CPD in producing improved quality design documentation? This provided the premise for the study and helped in finalising the arguments of the study by testing the hypothesis.
4.1.1 Respondents’ background information.
The study involved 64 (27%) project managers, 51 (22%) quantity surveyors, 35 (15%) engineers and 35 (15%) project supervisors, respectively; 31 (13%) architects; 13 (5%) managing directors; and 6 (3%) clients. The diverse categories of experts in the study, coupled with their involvement in the production of design documentation, indicate that reliable data was provided. In terms of education level, 55% were first-degree or honours graduates, 22% were master’s or postgraduate diploma holders, 18% were higher diploma holders and 2% were PhD holders. Therefore, the respondents have attained good levels of education and provided reliable data. A majority of 45% had between 11 and 20 years of experience in the industry, 38% had between 6 and 10 years, 11% had between 1 and 5 years and 6% had between 21 and 30 years of experience. This indicates their significant experience in the industry and, therefore, their provision of relevant responses. In terms of respondents’ sector of work, 49.79% work in the private sector, whereas 50.21% work within the public sector. When considering their work outfits, 65% work in consultants’ outfits, whereas 35% work in contractors’ outfits. Therefore, there is nearly a fair balance between respondents in the private and public sectors of work.
4.1.2 Validity, reliability and generalisability of study data.
Cronbach’s alpha (α) values were used to determine the extent to which the questionnaire yielded consistent findings that can be replicated transparently. This was achieved by the use of IBM SPSS software and the interpretation is based on the following values according to Tavakol and Dennick (2011); α ≥ 0.9 (Excellent); 0.9 > α ≥ 0.8 (Good); 0.8 > α ≥ 0.7 (Acceptable); 0.7 > α ≥ 0.6 (Questionable); 0.6 > α ≥ 0.5 (Poor); and 0.5 > α (Unacceptable). A value more than 0.7 is usually acceptable. Most often, higher values of alpha (0.90–0.95) which indicates highly correlated items are preferred. This notwithstanding, Table 1 presents the reliability statistics of data (20 items) on the capabilities required of the CPD approach.
The actual value of alpha is 0.994 which is greater than 0.9 (i.e. α > 0.9) indicating an excellent level of internal consistency. Therefore, the complete data is reliable and acceptable.
4.1.3 Preliminary analysis of questionnaire survey data.
A preliminary analysis of the questionnaire data was performed to determine the significance of each factor using RII for the ranking based on values ranging from 0 to 1. The ranking is based on five important levels which are 0.8 ≤ RII ≤ 1 (High, H); 0.6 ≤ RII ≤ 0.8 (High-Medium, H-M); 0.4 ≤ RII ≤ 0.6 (Medium, M); 0.2 ≤ RII ≤ 0.4 (Medium-Low, M-L); and 0 ≤ RII ≤ 0.2 (Low, L). However, where the RII values are the same, the mean score is used for the ranking. A total of 20 factors from the questionnaire survey were ranked and all had RII values between 0.85 and 0.89 (i.e. 0.8 ≤ RII ≤ 1) indicating high importance level. These factors indicate significant capabilities that are required of the CPD approach in producing design documentation. The top five ranked capabilities include preparation of detailed design; improving collaboration between architectural and engineering design disciplines; paying appropriate fees to consultants; specialists’ involvement in design planning and processing; and holding consultants accountable for producing poor design documentation quality.
4.1.4 Spearman’s rank-order correlation analysis.
The Spearman’s correlation is a non-parametric correlation analysis that involves determining the degree of correlation, the significance level and testing the correlation coefficient hypotheses. The statistical hypothesis test for the ρ values is H0: ρ (rho) = 0 (no relationship between the variables). H1: ρ (rho) ≠ 0 (relationship exists between the variables). The standard value for ρ is 0.05 or smaller at the 0.01 level (two-tailed) to consider the correlation statistically highly significant. The analysis involves 20 variables with 235 number of cases as presented in Table 2 including their mean and standard deviation values. From Table 2, the least standard deviation is 0.567 and the highest is 0.862. These values are relatively high which indicates statistical significance.
The codes in Table 2 are used to represent the capabilities in Tables 3 and 5.
4.1.4.1 Correlation analysis (rs).
Table 3 presents the correlation results in a matrix form. The least Spearman’s correlation coefficient (rs) value from the data set is +0.715 and the highest is +0.995 which are all very close to +1 (strong, positive correlation). The ρ values for all the 20 variables are 0.000 which is far less than the 0.01 level (two-tailed) and 0.05, therefore, there is evidence that a statistically significant bivariate association exists between the variables. From Table 3, all the ρ values are less than 0.01 and not equal to zero, therefore, in testing the population hypothesis, H1: ρ (rho) ≠ 0; hence, the null hypothesis is rejected and the alternative hypothesis is accepted that the relationship between the variables is statistically significant. Therefore, Spearman’s correlation coefficient, rs of +0.715 is statistically significant (p = 0.000).
4.1.4.2 Summary of the Spearman’s rank-order correlation.
In determining the relationship between the 20 variables on capabilities required in the production of design documentation, a Spearman’s rank-order correlation analysis was executed. The results in Table 3 shows that there is a strong, positive correlation between the variables, which is statistically significant [rs(20) = +0.715, p = 0.000].
4.1.5 Multiple regression analysis.
In determining the degree of relationship between the variables, the multiple regression analysis was carried out which resulted in testing the study hypothesis. The null hypothesis (H0) is the quality of design documentation will not improve when the required capabilities are determined. The alternative hypothesis (HA) is the quality of design documentation will improve when the required capabilities are determined. The regression analysis includes summary analysis: analysis of variance (ANOVA) and coefficients. A total of 20 capabilities were used as presented in Table 5 with a sample size of 235 without any missing score.
4.1.5.1 Summary analysis.
The summary analysis provides the R, R-square, adjusted R-square and the standard error of the estimate as presented in Table 4. These values determine how a regression analysis fits the data.
From Table 4, the multiple correlation coefficient (R) using all the predictors simultaneously is 0.886. The adjusted R-square is 0.766 which means that 76.6% of the variance in the capabilities required in the production of design documentation can be predicted from the combination of all the independent variables.
4.1.5.2 Analysis of variance.
A 95% confidence level which represents a 5% (0.05) level of significance was set for the regression. Therefore, from Table 4, a sig. value of 0.001 which is less than 0.05 is acceptable. The analysis is considered efficient and acceptable because the F-value of 39.220 in this case is greater than 1. From the regression analysis ANOVA in Table 4, there is a good fit for the data because the independent variables statistically significantly predict the dependent variable, F(20, 214) = 188.300, p < 0.05. This shows that an F-value of 39.220 is statistically significant and indicates that the predictors significantly combine to predict the capabilities required in the production of design documentation.
4.1.5.3 Regression analysis coefficients.
From Table 5, the statistical significance (sig.) and the t-values of each variable are important to determine the variables that significantly contributes to predict the capabilities required in the production of design documentation. For a 95% confidence level, the sig. value should be less than 0.05 (p < 0.05) to conclude that the coefficients are statistically significantly different to zero hence the null hypothesis can be rejected.
From Table 5, when all the predictors are considered, six variables have sig. values less than 0.05 (p < 0.05) which significantly adds to the prediction. These variables include incentives for producing good design documentation quality; improving consultants’ working conditions and procedures; mandatory coordination of design documentation by a supervisor with requisite skills and knowledge; improving collaboration between consultants; job-relevant training and practice; and preparation of detailed design. Therefore, these six variables reject the null hypothesis (H0) and accept the alternative hypothesis (HA) that the quality of design documentation will improve when the required capabilities are determined. However, all the predictors inclusively gave this result because the overall F-value was computed using all the variables.
4.1.5.4 Summary of regression results.
The results of the multiple regression shows that the combination of variables to predict the capabilities required in the production of design documentation from the predictor variables is statistically significant, F(20, 214) = 188.300, p < 0.05. There are six independent variables that significantly predict the capabilities required in the production of design documentation when all 20 variables are included. These variables are incentives for producing good design documentation quality; improving consultants working conditions and procedures; mandatory coordination of design documentation by a supervisor with requisite skills and knowledge; improving collaboration between consultants; job-relevant training and practice; and preparation of detailed design. The adjusted R2 value of 0.766 presented in Table 4 is an indication that 76.6% of the variance in the capabilities required in producing design documentation was explained.
4.2 Qualitative data analysis
Qualitative data for this study is obtained from 15 interviews with experts in the construction industry. These experts are professional quantity surveyors, architects and project managers. In analysing the data, content analysis was used with focus on descriptive and pattern coding with the aid of QSR Nvivo 12 Pro software. This approach helped to code themes and words in the interview transcripts and provided a systematic analysis of the data. Pseudonyms as used in Table 6 (P1, P2… and P15 which represents Participant 1, Participant 2 in that order up to Participant 15) were assigned to each interview transcript to ensure confidentiality and anonymity of participants.
4.2.1 Research participants background information.
Study participants include five quantity surveyors, five architects and five project managers, who provided expert opinions and reliable data. They have good levels of education therefore provided intellectual data for this study. A majority of 73% have master’s degrees while 27% are first degree graduates. Participants have rich experiences in the construction industry with in-depth knowledge on the study objective. The highest years of experience recorded is 21 while the least is 8 with an average of 14 years’ experience for all. However, eight participants (53%) work within the public sector, whereas seven (47%) works within the private sector. This provided a balance of information from the two sectors of work in the industry. Ten participants (67%) work within consultants’ outfit, whereas five (33%) work within contractors’ outfit. Therefore, there is a balance of information from both consultants and contractors.
4.2.2 Capabilities required of the conventional project delivery approach.
The raw data from the interview transcripts were auto coded with the aid of QSR Nvivo 12 Pro. This helped in coding the data and resulted in the extraction of interview responses verbatim for further analysis. This was followed by the application of content analysis which involves:
the selection of content to analyse: the interview transcripts;
definition of units: frequency of phrases and themes in the data; and
coding: appropriately recording data to determine descriptions and patterns.
The analysis resulted in identifying 16 capabilities as presented in Table 6 with a determination of the significance of each code based on the frequency of occurrence of each code.
The results indicate the following: all participants purported an improvement in collaboration between architectural and engineering design disciplines as a major capability. Seven participants were of the view that payment of appropriate fees to design consultants is a major capability. Six participants indicated that holding consultants accountable for producing poor design documentation quality and providing incentives for producing good design documentation quality are some potential capabilities. The top five factors ranked based on the frequency of response are improving collaboration between architectural and engineering design disciplines; paying appropriate fees to consultants; holding consultants accountable for producing poor design documentation quality; incentives for producing good design documentation quality; and provision of an elaborate and improved project brief.
5. Results and discussion
Identifying capabilities that the CPD approach must acquire in practice, particularly during design documentation production, to improve quality is the aim of this study. The results provide enough information to reject the null hypothesis and conclude that the quality of design documentation will improve when the required capabilities are determined. The questionnaire survey identified 20 capabilities, whereas the interviews identified 16 capabilities. However, 13 of these capabilities align with the questionnaire survey. Results from the interview participants who work in the private sector present three new capabilities that were not part of the questionnaire survey: contractors should make input during the design stage, designers should collaborate with all stakeholders during design and documentation and there should be enough effort towards reducing the level of fragmentation. A majority of 71% of participants who work in the public sector indicated that incentives for producing good design documentation quality must be formulated, whereas 64% believe that there is a need to improve collaboration between consultants. The main findings from the study are presented in Table 7, comprising six findings from the questionnaire survey and three additional findings from the interviews. These findings are discussed as follows.
5.1 Incentives for producing good design documentation quality
One major required capability is the introduction and formulation of incentives for producing good design documentation quality. This will help professionals to concentrate on the project and will result in unnecessary negligence on the part of designers (Dosumu and Aigbavboa, 2018). When there is no motivation, productivity is low. Therefore, there is the need for incentives to be introduced for producing better quality design documentation (Dosumu et al., 2017). Designers should also be paid adequately for tasks assigned as a motivation to be more productive (Dosumu and Aigbavboa, 2018).
5.2 Improving consultants’ working conditions and procedures
Improvement in the working conditions and procedures of consultants includes involving consultants early in the project especially during design to enhance better planning and project understanding (Ling et al., 2020; Agbaxode et al., 2020; Ma et al., 2018). They should be encouraged to work as a team rather than as individuals (Maskil-Leitan and Reychav, 2018). They should be held jointly accountable for errors and deficiencies in design and documentation (Agbaxode et al., 2020). Behaviourally, they should have mutual trust and respect for each other to improve better team coordination (Durdyev et al., 2019). They should promote effective collaboration to achieve better quality project delivery with enhanced project efficiency (Pishdad-Bozorgi, 2017).
5.3 Mandatory co-ordination of design documentation by a supervisor
Mandatory coordination of design documentation by a supervisor with requisite skills and knowledge is another important required capability. When coordination of the design process by a supervisor with the requisite expertise is mandatory, there will be an improvement in the quality of design documentation. This will help reduce conflicts and contradictions among drawings and other documentation provided (Assaf et al., 2017). Furthermore, due to the multi-disciplinary nature of the design team, there is the need to coordinate the activities of the various professionals to achieve an improvement in the quality of design documentation (Akampurira and Windapo, 2019).
5.4 Improving collaboration between consultants
There is a need for an improvement in collaboration between various design disciplines. Any improvement in collaboration between these professionals will result in a better understanding of each other and subsequently improve documentation quality because designs and drawings serve as the basis for the other documents (Agbaxode et al., 2023; Dosumu and Aigbavboa, 2018). A good collaboration will enhance knowledge sharing on the project which will result in improved design documentation quality (Dosumu and Aigbavboa, 2018) and will prevent deficiencies in design documentation (Assaf et al., 2017). This is consistent with Yap and Skitmore (2018) and Philips-Ryder et al. (2013) that the CPD lacks coordination between the design team members. Therefore, there is the need to ensure that they are coordinated to enhance project quality.
5.5 Job-relevant training and practice
The training of design team members is another significant capability. The specific duties or tasks that need to be performed by each professional must be clear and there must be a better understanding of the project to be executed (Dosumu and Aigbavboa, 2018). This can be achieved by holding job-specific trainings for the various project professionals. The training would help in knowledge sharing on their experiences (Dosumu and Aigbavboa, 2018). Furthermore, it will result in knowledge and experience transfer among designers (Dosumu et al., 2017). Therefore, designers’ professional education must be carried out to equip them with the requisite knowledge (Dosumu et al., 2017).
5.6 Preparation of detailed design
Design consultants must take their time and produce detailed designs. Detailing of designs will enhance documentation quality and subsequently improve project efficiency (Agbaxode et al., 2023). It gives other professionals a clear understanding of what is to be done and results in better documentation quality from all the other team members. It will reduce oversight problems and negligence on the side of some designers (Dosumu and Aigbavboa, 2018). Detailed designs have the tendency to ensure an efficient project delivery because the level of uncertainty will be reduced to a minimum. It will also help reduce design deficiencies because the design documents will portray adequate details (Assaf et al., 2017).
5.7 Allowing contractors to make input during the design stage
Encouraging contractors’ input during the design stage is considered another major capability that is required of the CPD approach. Interview participant 5 indicated that “…. Contractors should be allowed to make input and bring their experience to bear particularly during the design stage….”. Their early involvement is a very critical and significant element in project delivery (Agbaxode et al., 2020; Jadidoleslami et al., 2019). It ensures better planning and understanding of the project which enhances the quality of design documentation (Ling et al., 2020; Viana et al., 2020; Agbaxode et al., 2020; Ma et al., 2018). It allows for early contribution and sharing of knowledge and expertise at the project planning and design stages which results in a more effective, feasible and constructible design (Viana et al., 2020; Jadidoleslami et al., 2019). It helps in reducing possible reworks at the construction stage (Viana et al., 2020). It also helps in reducing claims and consequently minimises design changes throughout the project life (Kahvandi et al., 2016).
5.8 Designers collaborating with all stakeholders during design and documentation
Collaboration among all stakeholders during design documentation production will prevent deficiencies (Assaf et al., 2017). It will prevent project team members from working in isolation but rather work as a team to pursue the interest of the project rather than individual interest (Durdyev et al., 2019). Interview participant 12 indicated that “[…] designers must collaborate more with project stakeholders during design and even during the documentation stage […]”. This resonates with a research by Heravi et al. (2015) which evaluated the level of stakeholder involvement during the project planning stages and indicated that most project designers contribute less during the planning stage while the contractor is not involved in most cases. Therefore, there is a need for collaboration among designers and other stakeholders particularly during the early stages of the project to enhance the quality of design and documentation.
5.9 Appropriate efforts towards reducing the level of fragmentation
The study affirms that the CPD approach is characterised by fragmentation leaving the project team members and firms on their own. Interview participant 15 indicated that “…. there should be enough effort towards reducing the level of fragmentation that is associated with the conventional approach”. This aligns with Dosumu et al. (2017) that the construction industry is fragmented particularly under the CPD approach. Durdyev et al. (2019) asserts that this is a result of a lack of integration with this approach. Project team members are allowed into the project at different stages under this approach (Ebrahimi and Dowlatabadi, 2019). Therefore, design team members should coordinate and collaborate in executing their tasks to achieve effective project delivery (Akampurira and Windapo, 2019). In achieving this, there is a need to overcome the existence of multiple cultures within the CPD approach (Viana et al., 2020). However, the fragmentation with this approach results in poor productivity, delays, upsurges in cost and poor quality of projects (Zhang et al., 2018).
6. Conclusion
The study used the Ghanaian Construction Industry to identify specific capabilities that are required of the CPD in practice, particularly during the production of design documentation. As a result, specific capabilities were identified based on both questionnaire survey and interview data. The questionnaire survey identified 20 potential capabilities, and the interview responses agreed with this outcome, identifying 16 capabilities. However, 13 of these capabilities aligned with the outcome of the questionnaire survey while 3 additional capabilities were identified through the interviews that were not part of the questionnaire survey. After testing the hypothesis and finalising the argument for the study, 9 main required capabilities are presented. The specific findings include providing incentives for producing good design documentation quality; mandatory coordination of design documentation; improving collaboration among designers; and allowing contractors to make input during the design stage. The results indicate the need for the identified capabilities to be introduced in the CPD approach to improve design documentation quality and provide data for future research. The study offers a significant insight into the specific capabilities that are required of the CPD approach in practice, particularly in the production of design documentation. When considering current industry practices and regulations, the findings of this study re-echo the need to enforce compliance with regulations, codes and standards in the production of design documentation. This involves the mandatory co-ordination of design documentation by a supervisor and enhancing the preparation of detailed design. Professional bodies must continuously organise job-relevant training and practice for members, such as the use of various digital tools and technologies to enhance design documentation quality.
7. Limitations and recommendations for future research
This study has certain limitations despite the significant contribution it makes. One such limitation is that only 15 participants were interviewed. In future studies, a larger number of participants should be interviewed to collect a more diverse range of subjective views. Another limitation is that this study is focused on identifying specific capabilities required by the CPD in practice, particularly during the production of design documentation. Further research is recommended based on this study to establish an implementation framework for these capabilities within the CPD approach, aiming to improve the quality of design documentation. However, there is a larger research project underway that aims to develop a framework for improving the quality of design documentation. This study shares a common methodology, research respondents and participants with that larger project.
Reliability statistics of data
| Cronbach’s alpha | Cronbach’s alpha based on standardised items | No. of items |
|---|---|---|
| 0.993 | 0.994 | 20 |
Source: Authors’ own work
Descriptive statistics of data (N = 235)
| Codes | Capabilities | Mean | SD |
|---|---|---|---|
| C1 | Holding consultants accountable for producing poor design documentation quality (penalty) | 4.39 | 0.627 |
| C2 | Incentives for producing good design documentation quality | 4.30 | 0.567 |
| C3 | Paying appropriate fees to consultants | 4.43 | 0.659 |
| C4 | Improving consultants working conditions and procedures | 4.31 | 0.730 |
| C5 | Clients allowing adequate time for the preparation of design documents | 4.34 | 0.688 |
| C6 | Mandatory co-ordination of design documentation by a supervisor with requisite skills and knowledge | 4.33 | 0.686 |
| C7 | Independent review of design documentation by a supervisor with requisite skills and knowledge | 4.35 | 0.744 |
| C8 | Encouraging self-check practice and independent verification | 4.31 | 0.728 |
| C9 | Re-checking design documentation for accuracy | 4.33 | 0.721 |
| C10 | Use of checklists | 4.24 | 0.786 |
| C11 | Setting up quality control departments | 4.26 | 0.789 |
| C12 | Setting up minimum quality and service standards | 4.32 | 0.798 |
| C13 | Designers partnering with others while preparing design documents | 4.34 | 0.758 |
| C14 | Improving collaboration between consultants | 4.44 | 0.613 |
| C15 | Specialists’ involvement in design planning and processing | 4.43 | 0.626 |
| C16 | Use of computer programs such as BIM | 4.25 | 0.862 |
| C17 | Job-relevant training and practice | 4.23 | 0.812 |
| C18 | Provision of elaborate and improved project brief | 4.24 | 0.749 |
| C19 | Systematic audit to ensure the quality of briefs | 4.29 | 0.833 |
| C20 | Preparation of detailed design | 4.46 | 0.791 |
Source: Authors’ own work
Spearman’s correlation matrix (N = 235); sig. values are all 0.000
| Capabilities | Values | C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 | C12 | C13 | C14 | C15 | C16 | C17 | C18 | C19 | C20 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| C1 | rho | 1.000 | 0.817** | 0.886** | 0.943** | 0.965** | 0.946** | 0.941** | 0.939** | 0.969** | 0.886** | 0.923** | 0.925** | 0.946** | 0.904** | 0.918** | 0.957** | 0.903** | 0.862** | 0.926** | 0.737** |
| C2 | rho | 0.817** | 1.000 | 0.725** | 0.833** | 0.813** | 0.837** | 0.769** | 0.828** | 0.803** | 0.855** | 0.825** | 0.756** | 0.773** | 0.739** | 0.751** | 0.781** | 0.831** | 0.889** | 0.756** | 0.606** |
| C3 | rho | 0.886** | 0.725** | 1.000 | 0.862** | 0.881** | 0.863** | 0.926** | 0.860** | 0.886** | 0.820** | 0.850** | 0.927** | 0.918** | 0.981** | 0.965** | 0.883** | 0.837** | 0.798** | 0.916** | 0.836** |
| C4 | rho | 0.943** | 0.833** | 0.862** | 1.000 | 0.978** | 0.995** | 0.928** | 0.995** | 0.967** | 0.940** | 0.977** | 0.912** | 0.933** | 0.868** | 0.881** | 0.939** | 0.955** | 0.919** | 0.912** | 0.745** |
| C5 | rho | 0.965** | 0.813** | 0.881** | 0.978** | 1.000 | 0.973** | 0.948** | 0.974** | 0.988** | 0.922** | 0.957** | 0.932** | 0.953** | 0.888** | 0.901** | 0.960** | 0.937** | 0.901** | 0.932** | 0.760** |
| C6 | rho | 0.946** | 0.837** | 0.863** | 0.995** | 0.973** | 1.000 | 0.922** | 0.991** | 0.962** | 0.934** | 0.971** | 0.906** | 0.926** | 0.870** | 0.883** | 0.933** | 0.950** | 0.913** | 0.906** | 0.736** |
| C7 | rho | 0.941** | 0.769** | 0.926** | 0.928** | 0.948** | 0.922** | 1.000 | 0.929** | 0.956** | 0.883** | 0.916** | 0.983** | 0.991** | 0.935** | 0.949** | 0.945** | 0.898** | 0.862** | 0.983** | 0.801** |
| C8 | rho | 0.939** | 0.828** | 0.860** | 0.995** | 0.974** | 0.991** | 0.929** | 1.000 | 0.972** | 0.946** | 0.982** | 0.918** | 0.938** | 0.866** | 0.878** | 0.944** | 0.961** | 0.925** | 0.918** | 0.754** |
| C9 | rho | 0.969** | 0.803** | 0.886** | 0.967** | 0.988** | 0.962** | 0.956** | 0.972** | 1.000 | 0.922** | 0.956** | 0.945** | 0.965** | 0.893** | 0.906** | 0.971** | 0.936** | 0.900** | 0.944** | 0.774** |
| C10 | rho | 0.886** | 0.855** | 0.820** | 0.940** | 0.922** | 0.934** | 0.883** | 0.946** | 0.922** | 1.000 | 0.965** | 0.891** | 0.892** | 0.825** | 0.835** | 0.922** | 0.974** | 0.965** | 0.898** | 0.737** |
| C11 | rho | 0.923** | 0.825** | 0.850** | 0.977** | 0.957** | 0.971** | 0.916** | 0.982** | 0.956** | 0.965** | 1.000 | 0.921** | 0.924** | 0.856** | 0.867** | 0.950** | 0.978** | 0.935** | 0.925** | 0.758** |
| C12 | rho | 0.925** | 0.756** | 0.927** | 0.912** | 0.932** | 0.906** | 0.983** | 0.918** | 0.945** | 0.891** | 0.921** | 1.000 | 0.979** | 0.935** | 0.948** | 0.951** | 0.905** | 0.866** | 0.987** | 0.815** |
| C13 | rho | 0.946** | 0.773** | 0.918** | 0.933** | 0.953** | 0.926** | 0.991** | 0.938** | 0.965** | 0.892** | 0.924** | 0.979** | 1.000 | 0.927** | 0.940** | 0.954** | 0.907** | 0.871** | 0.978** | 0.799** |
| C14 | rho | 0.904** | 0.739** | 0.981** | 0.868** | 0.888** | 0.870** | 0.935** | 0.866** | 0.893** | 0.825** | 0.856** | 0.935** | 0.927** | 1.000 | 0.984** | 0.889** | 0.841** | 0.802** | 0.924** | 0.818** |
| C15 | rho | 0.918** | 0.751** | 0.965** | 0.881** | 0.901** | 0.883** | 0.949** | 0.878** | 0.906** | 0.835** | 0.867** | 0.948** | 0.940** | 0.984** | 1.000 | 0.901** | 0.852** | 0.812** | 0.936** | 0.805** |
| C16 | rho | 0.957** | 0.781** | 0.883** | 0.939** | 0.960** | 0.933** | 0.945** | 0.944** | 0.971** | 0.922** | 0.950** | 0.951** | 0.954** | 0.889** | 0.901** | 1.000 | 0.947** | 0.890** | 0.964** | 0.791** |
| C17 | rho | 0.903** | 0.831** | 0.837** | 0.955** | 0.937** | 0.950** | 0.898** | 0.961** | 0.936** | 0.974** | 0.978** | 0.905** | 0.907** | 0.841** | 0.852** | 0.947** | 1.000 | 0.940** | 0.920** | 0.754** |
| C18 | rho | 0.862** | 0.889** | 0.798** | 0.919** | 0.901** | 0.913** | 0.862** | 0.925** | 0.900** | 0.965** | 0.935** | 0.866** | 0.871** | 0.802** | 0.812** | 0.890** | 0.940** | 1.000 | 0.866** | 0.715** |
| C19 | rho | 0.926** | 0.756** | 0.916** | 0.912** | 0.932** | 0.906** | 0.983** | 0.918** | 0.944** | 0.898** | 0.925** | 0.987** | 0.978** | 0.924** | 0.936** | 0.964** | 0.920** | 0.866** | 1.000 | 0.812** |
| C20 | rho | 0.737** | 0.606** | 0.836** | 0.745** | 0.760** | 0.736** | 0.801** | 0.754** | 0.774** | 0.737** | 0.758** | 0.815** | 0.799** | 0.818** | 0.805** | 0.791** | 0.754** | 0.715** | 0.812** | 1.000 |
**Correlation is significant at the 0.01 level (two-tailed)
Source: Authors’ own work
Regression analysis summary and ANOVA
| Summary | R | R square | Adjusted R square | Std. error of the estimate |
|---|---|---|---|---|
| 0.886 | 0.786 | 0.766 | 13.722 |
| ANOVA | Model | Sum of squares | df | Mean square | F | Sig |
| Regression | 147,703.782 | 20 | 7,385.189 | 39.220 | <0.001 | |
| Residual | 40,296.218 | 214 | 188.300 | |||
| Total | 188,000.000 | 234 |
Source: Authors’ own work
Regression analysis coefficients
| Capabilities | Unstandardised coefficients | Standardised coefficients | |||
|---|---|---|---|---|---|
| B | Std. error | Beta | t | Sig. | |
| Constant | −130.323 | 8.380 | −15.552 | <0.001 | |
| C1 | 9.617 | 6.639 | 0.213 | 1.448 | 0.149 |
| C2 | 12.816 | 4.420 | 0.256 | 2.899 | 0.004 |
| C3 | −13.425 | 7.476 | −0.312 | −1.796 | 0.074 |
| C4 | −49.342 | 9.843 | −1.271 | −5.013 | <0.001 |
| C5 | 9.590 | 9.826 | 0.233 | 0.976 | 0.330 |
| C6 | 52.952 | 13.295 | 1.281 | 3.983 | <0.001 |
| C7 | −1.318 | 10.175 | −0.035 | −0.129 | 0.897 |
| C8 | −18.847 | 15.362 | −0.484 | −1.227 | 0.221 |
| C9 | −19.028 | 10.279 | −0.484 | −1.851 | 0.066 |
| C10 | 0.959 | 7.886 | 0.027 | 0.122 | 0.903 |
| C11 | 9.249 | 10.181 | 0.257 | 0.908 | 0.365 |
| C12 | −8.083 | 9.794 | −0.228 | −0.825 | 0.410 |
| C13 | 14.049 | 11.644 | 0.376 | 1.207 | 0.229 |
| C14 | 26.493 | 9.591 | 0.573 | 2.762 | 0.006 |
| C15 | −9.670 | 9.868 | −0.213 | −0.980 | 0.328 |
| C16 | 7.189 | 6.410 | 0.218 | 1.121 | 0.263 |
| C17 | 16.524 | 7.831 | 0.473 | 2.110 | 0.036 |
| C18 | −4.298 | 6.351 | −0.114 | −0.677 | 0.499 |
| C19 | −7.619 | 8.765 | −0.224 | −0.869 | 0.386 |
| C20 | 15.620 | 3.155 | 0.436 | 4.951 | <0.001 |
Source: Authors’ own work
Capabilities required in the production of design documentation under the CPD approach
| Code | Capabilities | Participants responses | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| P1 | P2 | P3 | P4 | P5 | P6 | P7 | P8 | P9 | P10 | P11 | P12 | P13 | P14 | P15 | Frequency | Ranking | ||
| C14 | Improving collaboration between consultants | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | 15 | 1 |
| C3 | Paying appropriate fees to consultants | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | 7 | 2 | ||||||||
| C1 | Holding consultants accountable for producing poor design documentation quality (penalty) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | 6 | 3 | |||||||||
| C2 | Incentives for producing good design documentation quality | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | 6 | 4 | |||||||||
| C18 | Provision of elaborate and improved project brief | ✓ | ✓ | ✓ | ✓ | 4 | 5 | |||||||||||
| C20 | Preparation of detailed design and documentation | ✓ | ✓ | ✓ | 3 | 6 | ||||||||||||
| C13 | Designers partnering with others while preparing design documents | ✓ | ✓ | ✓ | 3 | 7 | ||||||||||||
| C16 | Use of computer programs such as BIM | ✓ | ✓ | ✓ | 3 | 8 | ||||||||||||
| C21 | Allowing contractors to make input during the design stage | ✓ | ✓ | 2 | 9 | |||||||||||||
| C5 | Clients allowing adequate time for the preparation of design documents | ✓ | ✓ | 2 | 10 | |||||||||||||
| C6 | Mandatory coordination of design documentation by a supervisor with requisite skills and knowledge | ✓ | ✓ | 2 | 11 | |||||||||||||
| C15 | Specialists’ involvement in design planning and processing | ✓ | 1 | 12 | ||||||||||||||
| C22 | Designers collaborating with all stakeholders during design and documentation | ✓ | 1 | 13 | ||||||||||||||
| C9 | Re-checking design documentation for accuracy | ✓ | 1 | 14 | ||||||||||||||
| C17 | Job-relevant training and practice | ✓ | 1 | 15 | ||||||||||||||
| C23 | Appropriate efforts towards reducing the level of fragmentation | ✓ | 1 | 16 | ||||||||||||||
Source: Authors’ own work
Capabilities required in the production of design documentation
| No. | Capabilities |
|---|---|
| Questionnaire and interview capabilities | Incentives for producing good design documentation quality |
| Improving consultants working conditions and procedures | |
| Mandatory coordination of design documentation by a supervisor | |
| Improving collaboration between consultants | |
| Job-relevant training and practice | |
| Preparation of detailed design | |
| New capabilities from interviews | Allowing contractors to make input during the design stage |
| Designers collaborating with all stakeholders during design and documentation | |
| Appropriate efforts towards reducing the level of fragmentation |
Source: Authors’ own work
References
Abdallah, A., Assaf, S. and Hassanain, M.A. (2018), “Assessment of the consequences of deficiencies in design documents in Saudi Arabia”, Architectural Engineering and Design Management, Vol. 15 No. 4, pp. 1-15, doi: 10.1080/17452007.2018.1561412.
Addy, M., Adinyira, E. and Ayarkwa, J. (2018), “Antecedents of building information modelling adoption among quantity surveyors in Ghana: an application of a technology acceptance model”, Journal of Engineering, Design and Technology, Vol. 16 No. 2, pp. 313-326, doi: 10.1108/JEDT-06-2017-0056.
Agbaxode, P., Dlamini, S. and Saghatforoush, E. (2021b), “Design documentation quality influential variables in the construction sector”, IOP Conference Series: Earth and Environmental Science, Vol. 654 No. 1, p. 012007, doi: 10.1088/1755-1315/654/1/012007.
Agbaxode, P.D.K., Dlamini, S. and Saghatforoush, E. (2021a), “Quality of design documentation in the construction industry: a review using meta-synthesis approach”, International Journal of Innovation and Technology Management, Vol. 18 No. 6, p. 2130003, doi: 10.1142/S0219877021300032.
Agbaxode, P.D.K., Saghatforoush, E. and Dlamini, S. (2020), “Integrated project delivery (IPD): projecting a common good to key participants and the project”, in Scott, L. and Neilson, C.J. (Eds), Proceedings of the 36th Annual ARCOM Conference, 7-8 September 2020. Association of Researchers in Construction Management, UK, pp. 136-145, available at: http://www.arcom.ac.uk/-docs/proceedings/1fad0315c84c47cdedb7ba2cbed175f8.pdf
Agbaxode, P.D., Saghatforoush, E. and Dlamini, S. (2023), “Assessment of the impact of design documentation quality on construction project delivery”, Journal of Engineering, Project and Production Management, Vol. 13 No. 2, pp. 81-92, doi: 10.32738/JEPPM-2023-0009.
Akampurira, E. and Windapo, A. (2018), “Factors influencing the quality of design documentation on South African civil engineering projects”, Journal of the South African Institution of Civil Engineering, Vol. 60 No. 3, doi: 10.17159/2309-8775/2018/v60n3a4.
Akampurira, E. and Windapo, A. (2019), “Key quality attributes of design documentation: South African perspective”, Journal of Engineering, Design and Technology, Vol. 17 No. 2, pp. 362-382, doi: 10.1108/JEDT-08-2018-0137.
Aldossari, K.M., Lines, B.C., Smithwick, J.B., Hurtado, K.C. and Sullivan, K.T. (2021), “Alternative project delivery method adoption in the AEC industry: an organizational change perspective”, International Journal of Construction Education and Research, Vol. 19 No. 2, pp. 150-165, doi: 10.1080/15578771.2021.2013997.
Aldrich, J.O. (2018), Using IBM SPSS Statistics: An Interactive Hands-on Approach, Sage Publications.
Ameyaw, C. and Oteng-Seifah, S. (2010), “Construction project delivery in Ghana: the performance of the traditional procurement method”, West Africa Built Environment Research (Waber) Conference, p. 255.
Ashcraft, H. (2022), “Transforming project delivery: integrated project delivery”, Oxford Review of Economic Policy, Vol. 38 No. 2, pp. 369-384.
Assaf, S., Hassanain, M.A. and Abdallah, A. (2017), “Assessment of deficiencies in design documents for large construction projects”, Journal of Performance of Constructed Facilities, Vol. 31 No. 5, p. 04017086.
Brown, J.T. (2002), “Controlling costs using design quality workshops”, AACE International Transactions, Morgantown, 10, pp. CS101-CS109.
Buertey, J.T., Dadadzogbor, E. and Atsrim, F. (2021), “Procurement path influencing factors in Ghana: managing the challenge of cultural shift”, International Journal of Construction Management, Vol. 21 No. 1, pp. 78-92.
CMAA (2012), “An owner’s guide to project delivery methods”, CMAA (Construction Management Association of America) McLean, VA [Preprint], (Advancing Professional Construction and Program Management Worldwide).
Construction Industry Council (UK) (2002), “Design quality indicator (DQI)”, Construction Industry Council, available at: http://www.dqi.org.uk/ (1 of 2) (accessed 15 January 2003 15:34:50).
Creswell, J.W. and Creswell, J.D. (2017), Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, Sage publications.
Denzin, N.K. (2012), “Triangulation 2.0”, Journal of Mixed Methods Research, Vol. 6 No. 2, pp. 80-88.
Denzin, N.K. and Lincoln, Y.S. (2011), The Sage Handbook of Qualitative Research, Sage.
Dosumu, O. and Aigbavboa, C. (2018), “An assessment of the causes, cost effects and solutions to design-error induced variations on selected building projects in Nigeria”, Acta Structilia, Vol. 25 No. 1, doi: 10.18820/24150487/as25i1.2.
Dosumu, O., Idoro, G. and Onukwube, H. (2017), “Causes of errors in construction contract documents in southwestern, Nigeria”, Journal of Construction Business and Management, Vol. 1 No. 2, pp. 11-23.
Dudwick, N., Kuehnast, K., Jones, V.N. and Woolcock, M. (2006), “Analyzing social capital in context”, A guide to using qualitative methods and data, pp. 1-46.
Durdyev, S., Hosseini, M.R., Martek, I., Ismail, S. and Arashpour, M. (2019), “Barriers to the use of integrated project delivery (IPD): a quantified model for Malaysia”, Engineering, Construction and Architectural Management, Vol. 27 No. 1, pp. 186-204, doi: 10.1108/ECAM-12-2018-0535.
Ebrahimi, G. and Dowlatabadi, H. (2019), “Perceived challenges in implementing integrated project delivery (IPD): insights from stakeholders in the U.S. and Canada for a path forward”, International Journal of Construction Education and Research, Vol. 15 No. 4, pp. 291-314.
Fish, A.J. and Keen, J. (2012), “Integrated project delivery: the obstacles of implementation”, ASHRAE Transactions, Vol. 118 No. 1, pp. 90-97.
George, D. and Mallery, P. (2019), IBM SPSS Statistics 26 Step by Step: A Simple Guide and Reference, Routledge.
Giddings, B., Sharma, M., Jones, P. and Jensen, P. (2013), “An evaluation tool for design quality: PFI sheltered housing”, Building Research and Information, Vol. 41 No. 6, pp. 690-705, doi: 10.1080/09613218.2013.775895.
Harper, C.M., Molenaar, K.R. and Cannon, J.P. (2016), “Measuring constructs of relational contracting in construction projects: the owner’s perspective”, Journal of Construction Engineering and Management, Vol. 142 No. 10, p. 04016053, doi: 10.1061/(ASCE)CO.1943-7862.0001169.
Harputlugil, T., Gültekin, A.T., Prins, M. and Topçu, Y.İ. (2014), “Architectural design quality assessment based on analytic hierarchy process: a case study”, Metu Journal of the Faculty of Architecture, Vol. 31 No. 2, doi: 10.4305/METU.JFA.2014.2.8.
Heravi, A., Coffey, V. and Trigunarsyah, B. (2015), “Evaluating the level of stakeholder involvement during the project planning processes of building projects”, International Journal of Project Management, Vol. 33 No. 5, pp. 985-997.
Holt, G.D. (2014), “Asking questions, analysing answers: relative importance revisited”, Construction Innovation, Vol. 14 No. 1, pp. 2-16, doi: 10.1108/CI-06-2012-0035.
Jadidoleslami, S., Saghatforoush, E., Heravi, A. and Preece, C. (2019), “A practical framework to facilitate constructability implementation using the integrated project delivery approach: a case study”, International Journal of Construction Management, Vol. 22 No. 7, pp. 1-15, doi: 10.1080/15623599.2019.1686834.
Kahvandi, Z., Saghatforoush, E., Alinezhad, M. and Preece, C. (2016), “Analysis of research trends on benefits of implementing integrated project delivery (IPD)”, International Conference on Civil, Mechanical Engineering and Construction Management, p. 11.
Ling, F.Y.Y., Teo, P.X., Li, S., Zhang, Z. and Ma, Q. (2020), “Adoption of integrated project delivery practices for superior project performance”, Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, Vol. 12 No. 4, p. 05020014, doi: 10.1061/(ASCE)LA.1943-4170.0000428.
Love, P.E.D., Edwards, D.J., Han, S. and Goh, Y.M. (2011), “Design error reduction: toward the effective utilization of building information modeling”, Research in Engineering Design, Vol. 22 No. 3, pp. 173-187, doi: 10.1007/s00163-011-0105-x.
Ma, Z., Zhang, D. and Li, J. (2018), “A dedicated collaboration platform for integrated project delivery”, Automation in Construction, Vol. 86, pp. 199-209, doi: 10.1016/j.autcon.2017. doi: 10.024.
Maskil-Leitan, R. and Reychav, I. (2018), “A sustainable sociocultural combination of building information modeling with integrated project delivery in a social network perspective”, Clean Technologies and Environmental Policy, Vol. 20 No. 5, pp. 1017-1032, doi: 10.1007/s10098-018-1526-2.
Masterman, J. (2003), An Introduction to Building Procurement Systems, Routledge.
Mesa, H.A., Molenaar, K.R. and Alarcón, L.F. (2016), “Exploring performance of the integrated project delivery process on complex building projects”, International Journal of Project Management, Vol. 34 No. 7, pp. 1089-1101, doi: 10.1016/j.ijproman.2016.05.007.
Mohajan, H.K. (2017), “Two criteria for good measurements in research: validity and reliability”, Annals of Spiru Haret University. Economic Series, Vol. 17 No. 4, pp. 59-82, doi: 10.26458/1746.
Morgan, G.A., Barrett, K.C., Leech, N.L. and Gloeckner, G.W. (2019), IBM SPSS for Introductory Statistics: Use and Interpretation: Use and Interpretation, Routledge.
Nawi, M.N.M., Haron, A.T., Hamid, Z.A., Kamar, K.A.M. and Baharuddin, Y. (2014), “Improving integrated practice through building information modeling-integrated project delivery (BIM-IPD) for Malaysian industrialised building system (IBS) construction projects”, Building Information Modeling, Vol. 15, p. 16.
Pärn, E.A., Edwards, D.J. and Sing, M.C.P. (2018), “Origins and probabilities of MEP and structural design clashes within a federated BIM model”, Automation in Construction, Vol. 85, pp. 209-219, doi: 10.1016/j.autcon.2017.09.010.
Patterson, D.A., Farnsworth, C.B., Hutchings, D.M., Eggett, D.L. and Weidman, J.E. (2021), “Comparing cost, schedule, and quality of CM/GC and DBB project delivery for repetitive commercial construction”, International Journal of Construction Education and Research, Vol. 19 No. 2, pp. 166-186, doi: 10.1080/15578771.2021.2013998.
Philips-Ryder, M., Zuo, J. and Jin, X.H. (2013), “Evaluating document quality in construction projects – subcontractors’ perspective”, International Journal of Construction Management, Vol. 13 No. 3, pp. 77-94, doi: 10.1080/15623599.2013.10773217.
Pishdad-Bozorgi, P. (2017), “Case studies on the role of integrated project delivery (IPD) approach on the establishment and promotion of trust”, International Journal of Construction Education and Research, Vol. 13 No. 2, pp. 102-124.
Ribeiro, F.L. and Fernandes, M.T. (2010), “Exploring agile methods in construction small and medium enterprises: a case study”, Journal of Enterprise Information Management, Vol. 23 No. 2, pp. 161-180.
Rowlinson, S. and McDermott, P. (2005), Procurement Systems: A Guide to Best Practice in Construction, Routledge.
Salla, D.E. (2020), “Comparing performance quality of design-bid-build (DBB) and design-build (DB) project delivery methods in Nigeria”, African Journal of Earth and Environmental Sciences, Vol. 2 No. 2.
Saunders, M., Lewis, P. and Thornhill, A. (2016), Research Methods for Business Students, 7th ed., Pearson Education, Harlow.
Saunders, M.N.K., Lewis, P. and Thornhill, A. (2019), Research Methods for Business Students, 8th ed., Pearson Education, available at: http://gen.lib.rus.ec/book/index.php?md5=D9BE047037B2AD58E4A996E689A11A84 (accessed 9 September 2022).
Shoar, S. and Payan, S. (2021), “A qualitative system dynamics approach to modeling the causes and effects of design deficiencies in construction projects”, Journal of Facilities Management, Vol. 20 No. 4, pp. 558-569.
Tashakkori, A. and Teddlie, C. (2010), Sage Handbook of Mixed Methods in Social and Behavioral Research, Sage.
Tavakol, M. and Dennick, R. (2011), “Making sense of Cronbach’s alpha”, International Journal of Medical Education, Vol. 2, p. 53.
Tilley, P.A., Mcfallan, S.L. and Sinclair, R.G. (2002), “Improving design and documentation quality”, CIB REPORT, pp. 361-380.
Tuhacek, M. and Svoboda, P. (2019), “Quality of project documentation”, IOP Conference Series: Materials Science and Engineering, Vol. 471, p. 052012, doi: 10.1088/1757-899X/471/5/052012.
Viana, M.L., Hadikusumo, B.H., Mohammad, M.Z. and Kahvandi, Z. (2020), “Integrated project delivery (IPD): an updated review and analysis case study”, Journal of Engineering, Project, and Production Management, Vol. 10 No. 2, pp. 147-161, doi: 10.2478/jeppm-2020-0017.
Yap, J.B.H. and Skitmore, M. (2018), “Investigating design changes in Malaysian building projects”, Architectural Engineering and Design Management, Vol. 14 No. 3, pp. 218-238.
Zaneldin, E.K. (2020), “Investigating the types, causes and severity of claims in construction projects in the UAE”, International Journal of Construction Management, Vol. 20 No. 5, pp. 385-401.
Zeng, S., Tian, P. and Tam, C. (2005), “Quality assurance in design organisations: a case study in China”, Managerial Auditing Journal, Vol. 20 No. 7, pp. 679-690.
Zhang, L., Huang, S. and Peng, Y. (2018), “Collaboration in integrated project delivery: the effects of trust and formal contracts”, Engineering Management Journal, Vol. 30 No. 4, pp. 262-273, doi: 10.1080/10429247.2018.1498259.
Acknowledgements
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