Comparison of Utility Managers’ and Project Managers’ Satisfaction Rating of Alternative Project Delivery Methods Used in Water and Wastewater Infrastructures

Abstract

Since the 1990s, the use of design-build (DB) and construction manager at-risk (CMAR) as alternative project delivery (APD) methods for water and wastewater projects has significantly increased, fast outpacing the growth of traditional project delivery methods. Currently, U.S. municipalities face challenges of an aging infrastructure, which requires replacing and expanding water and wastewater facilities at more than 7,000 utilities nationwide. This study compared and analyzed the differences between utility managers (UMs) and project managers (PMs) regarding their satisfaction levels of the various benefits when using APD methods. Respondents included UMs and PMs having experience in DB and CMAR water and wastewater projects. In this study, PMs were significantly more satisfied with the quality of project, change orders, and lower rate of disputes than UMs. In addition, PMs experienced a significantly higher schedule advantage than UMs. Most respondents were satisfied with the various advantages provided by APD methods.

Keywords

construction management contracting customer service or satisfaction utilities wastewater sewer highways buildings

Introduction

U.S. water municipalities face significant challenges with regard to aging pipes, which result in 240,000 water main breaks per year, according to American Water Works Association (AWWA; American Society of Civil Engineers [ASCE], 2013). By the year 2020, the United States will need up to US$1.3 trillion in capital investments to repair and replace the water/wastewater infrastructure to maintain an adequate delivery of drinking water and to treat wastewater. Public owners and industry firms continuously seek improved methods for planning and implementing projects in less time, using more innovative design and construction and at reduced risk to all parties. Furthermore, the industry believes that the required investments can be more economical through applying new approaches to the design and construction of water and wastewater infrastructure using alternative project delivery (APD) methods, such as design-build (DB) and construction manager at-risk (CMAR). The increased use of APD methods will be possible only if the municipal owners are satisfied with the results in their projects.

It has been found that there are several risks factors to be considered while selecting DB delivery methods in highway infrastructure projects (Tran & Molenaar, 2014). Some of the important risks factors are scope risk, complexity risk, construction risk, utility risk and right-of-way risk, and level of design risk. In DB project, generally the percentage of design completed is between 10 and 30 (Shorney-Darby, 2011). The owner would like to limit the level of design completed in their bridging documents because it will help to create innovations during the construction phase. In DB projects, the majority of the owners used best value procurement compared with price-based procurement. In best value procurement, technical information from qualification-based selection phase will be incorporated with the price proposal to select a design builder.

In this study, a survey was conducted to determine the owners’ level of satisfaction and the resulting benefits when using APD methods in their projects. The major objectives of this study were as follows:

To determine the size, contract procurement, and contractor selection process for projects using APD methods;

To compare the levels of satisfaction of utility managers (UMs) and the owners’ project managers (PMs) with regard to the benefits they perceive in using APD methods in water and wastewater projects;

To compare the rankings provided by UMs and PMs regarding the main reasons for using APD methods in water and wastewater projects;

To compare the cost and schedule savings estimated by UMs and PMs; and

To compare the ratings of benefits the UMs and PMs received by using APD methods.

The scope of the study was limited to collecting the responses from the UMs and PMs involved in construction of water and wastewater projects. Survey participants are public officials and managers of local and regional governmental units, such as municipalities and water/wastewater districts in the United States. Initially, the list of respondents to be used in this study was received from the Water Design Build Council (WDBC). However, the research team expanded this list by contacting the water and wastewater owners all over United States by means of phone and email. In the end, the survey was sent to 455 participants.

Literature Review

In the past, three surveys have been conducted on the use of APD methods in water and wastewater projects. The first study was an industry-wide survey, conducted by Molenaar, Bogus, and Priestley (2004) between 2001 and 2002, that showed a rapid growth in the use of DB as an APD method for water and wastewater projects in the United States. This group stated, however, that many water and wastewater projects in different states still used traditional design-bid-build (DBB) method. The second survey, conducted by the WDBC (2009a), compared project performance of DB and DBB project delivery methods used in water and wastewater projects. Responses were received from 100 public utility owners, out of which 31 respondents were involved in DB projects and 69 respondents were involved in DBB projects completed from 2003 to 2008. Results showed that DB projects had lower schedule overruns during the design phase and the construction phase than DBB projects. Also, DB projects outperformed DBB projects in terms of construction costs spent per month. The third survey, also carried out by the WDBC (2009b), wanted to determine the various reasons for using the DB method in water and wastewater projects. In a telephone survey conducted among municipal representatives who were using DB method, most stated that the major benefits were (a) a single point of accountability, (b) the design builder’s involvement during design, (c) fast delivery of the project, and (d) the high quality of the completed projects.

Other studies conducted regarding the performance of DB in water and wastewater projects confirmed that the main benefit of the DB project delivery method was a single point of responsibility of the design builder for cost, schedule, and quality. Other benefits reported were fast delivery, few change orders, better quality, less owner risk, cost savings, and fewer claims and litigation (Arora, 2000; Miller et al., 2000). However, other APD methods—such as design-build-operate (DBO) and design-build-maintain (DBM)—also demonstrated such advantages as a single point of responsibility for construction and operation of the project, lower risks between the involved parties, lower costs, and schedule advantages (Beringer et al., 1999; Culp, 2011).

The successful completion of projects using APD methods is critical to increase their use in future projects. Chan, Scott, and Lam (2002) reported that the success of a DB project depended upon the level of satisfaction of owners and PMs based on timely delivery, the quality of the completed project, and the cost effectiveness of the project. Molenaar et al. (2004) concluded that a DB method could be used successfully in water and wastewater projects by providing proper contract documents, allocating risk to concerned parties, using best value methods to select the design builder, and building teamwork between the owner and the design builder. On the contrary, Arora (2000) commented that a DB method could be unsuccessful if the owners did not properly prepare the Request for Proposal (RFP) during the project procurement phase. In addition, the author stated that DB method could be unsuccessful if the owners did not focus on selecting the right consultant for the preparation of the contract documents and ensuring that the project had clear specifications and goals.

The main goals of owners always are to save cost and time by using innovative means to maintain the quality in their projects. According to Baird (2011), technical innovation in design and material selection when using APD methods has resulted in cost and time savings while maintaining the quality of projects. White, Jones, Waer, Hayes, and Quarendon (2005) noted that an innovative use of a smaller footprint when designing facilities reduced the size, complexity, and adverse effects on the native environment.

Regarding cost savings, DB and DBO project delivery methods have resulted in reducing costs for water and wastewater projects. In the 2008 survey by the WDBC, most respondents (89%) reported that they completed their projects on budget. The cost savings when building the Tolt Water Treatment Plant in Seattle, Washington, amounted to US$70 million (M); this project used the DBO method instead of a conventional DBB method (Kelly, Haskins, & Reiter, 1998). The design, construction, and operations for the Lake Pleasant Water Treatment Plant in Phoenix, Arizona, which used a DBO method combined with the experience of the city’s engineering and operations staff, saved US$30M compared with the city’s benchmark cost (White et al., 2005). Furthermore, projects using various types of APD methods were found to reduce costs when compared with the DBB approach (Culp, 2011). West Valley Construction Company, Inc. (WVC; 2013), located in Campbell, California, estimated a cost savings of 6% and reduction in claims and litigation by 60% in DB projects compared with the traditional projects.

Regarding time savings, surveys and studies have shown that the owners using DB as an APD method in their water and wastewater projects completed their projects before or on schedule (Culp, 2011; WDBC, 2009a, 2009b; WVC, 2013).

Survey Questionnaire and Data Collection

For this study, a survey questionnaire was prepared and emailed to 455 participants between October 1, 2012, and December 14, 2012. The list of the participants included the management- and project-level staff of water and wastewater owners. The authors prepared the mailing list by searching water and wastewater owners’ management-level personnel by personal contacts and through LinkedIn as well as from WDBC members’ list. Those who were qualified to take the survey were owners with experience in APD methods for their water and wastewater projects. There were 153 responses, representing a 35% response rate; 116 respondents stated that they were either UMs or PMs.

The parameters in the questionnaire were identified after conducting detailed literature review and getting feedback from WDBC personnel. WDBC has been conducting this type of survey since 2001, so it was helpful to get feedback from them. Also the authors of this study have experience in conducting these types of surveys. Qualtrics survey software was used to design the questionnaire because this software allows to collect and download the data in the spreadsheet formats compatible to Statistical Package for Social Science (SPSS). The questionnaire began with questions relating to general information regarding the respondents, including their titles, APD experience, the types and size of projects with which they were involved, and the processes they used for procurement and contractor selection. The major questions related to the satisfaction level with regard to benefits when using APD methods in water and wastewater projects. The respondents were asked to quantify the cost and schedule savings they received in their projects when using APD methods. In addition, respondents ranked the reasons they used an APD method for their projects.

Data Analysis

After collecting the data, descriptive statistics and statistical tests were used to analyze the data. Specifically, descriptive statistics were used to quantitatively describe the main features of the collected data, and SPSS was used to perform various statistical tests.

Descriptive Statistics

In this study, the Relative Importance Index (RII) was used to determine the ranking of reasons for using APD methods. The higher value of the RII, the more important the reason was for using the APD methods in water and wastewater projects.

RII was calculated using the formula shown in Equation 1:

RII = \frac{\sum W}{(A \times N)},

where W is the weight given to each reason by the respondents (ranging from 1 to 5); A is the highest weight, in this case, 5; and N is the total number of respondents. In this study, the RII value had a range of 0 to 1 (0 being not inclusive).

Statistical Tests

The statistical tests used in this study were Pearson’s chi-square test, parametric tests, and non-parametric tests. The decision as to which statistical test was used was based on the research design, the distribution of the data, and the type of variable. In general, parametric tests were chosen if the data distribution was normal, otherwise non-parametric tests were chosen.

The Anderson–Darling test was conducted to determine whether the population distributions of the dependent variables were normal. A null hypothesis of this test stated that the population distributions of the dependent variables were normal. If the p value for this test was less than or equal to .05, then the null hypothesis was rejected, confirming that the population distributions were not normal.

Pearson’s chi-square test was conducted when the dependent variables were categorical. In this study, this test was conducted to determine the group differences of some of the benefits when using APD methods. The null hypothesis for this test stated that there was no significant difference in responses between the two groups. If p value was less than .05, then the null hypothesis was rejected, confirming that there was a significant difference between the responses of the two groups.

Mann–Whitney U test, a non-parametric test, was conducted when the dependent variables were in an ordinal scale and the population distributions of the dependent variables were not normal. In this study, this test was used to determine the group differences, based on the type of respondent, regarding satisfaction with APD methods for their projects. The null hypothesis of this test was that there was no significant difference between the satisfaction levels of two groups. If the p value was less than or equal to .05, the null hypothesis was rejected, confirming the significant group difference.

The ANOVA test, a parametric test, was conducted when the dependent variables were in a ratio scale, the population distributions of the dependent variables were normal, and the population variances were equal. The ANOVA test was used to determine the significant differences between the cost and schedule growth estimated by two groups of respondents. The respondents estimated cost and schedule growth in terms of percentages. The null hypothesis of this test was that the mean cost and the schedule growth of two groups of respondents were not significantly different from each other. The confidence level selected for this data analysis was set at 95%. For the null hypothesis to be false, the p value needed to be less than or equal to .05. If the null hypothesis was rejected, there was a significant difference between the means of the two groups.

Levene’s test was conducted to find out whether the variances of the population distribution between two groups were equal. The null hypothesis for this test was that the population variances were equal. If the p value of the test showed a value greater than .05, then the null hypothesis was accepted, confirming that the sample had equal variances.

Descriptive Statistics Results

UMs and PMs who were involved in projects built with APD methods made up the sample of people who responded to the survey. As shown in Figure 1, PMs constituted the highest percentage (54%), followed by UMs (42%).

Figure 1.

Types of responsibilities of the respondents in this survey by percentage (n = 140).

When asked about the number of the projects they were involved in, the majority of the respondents were involved in more than one project. As shown in Figure 2, 39% were involved in more than five projects, 44% of the respondents were involved in two to five projects, and 17% of the respondents were involved in only one project.

Figure 2.

Number of respondents involved in projects using APD methods (n = 137).

Regarding project size, procurement process, and contractor selection methods, most respondents were involved in the projects having a total project cost between US$10M and US$100M. The percentage of respondents involved in projects with a total project cost less than US$10M, between US$10M and US$100M, and more than US$100M were 25%, 60%, and 15%, respectively, as shown in Figure 3. These results indicate that the majority of projects that used APD methods were medium-sized projects.

Figure 3.

Range of project costs by percentage (n = 132).

As shown in Figure 4, 68% of respondents were involved in a two-step solicitation process, a Request for Qualification (RFQ) and a RFP; 32% of the respondents were involved in one-step solicitation process, an RFP. The results showed that most owners preferred the two-step process to procure water and wastewater projects when using APD methods.

Figure 4.

Preference for a one-step or two-step solicitation process by percentage (n = 132).

When asked about the criteria for contractor selection that they used in their projects, 57% used “best value,” 31% used qualification only, and 12% used price selection as the criteria (Figure 5). The results showed that the “best value” method was the one most commonly used in projects built with APD methods.

Figure 5.

Number of respondents versus contract selection criteria (n = 131).

Comparison of Responses Based on Types of Respondents

For purposes of comparison, the responses were subdivided into two groups based on types of respondents. Details are described below.

Satisfaction Levels Regarding Benefits When Using APD Methods

Regarding the benefits of using APD methods, the Anderson–Darling Test (ADT) was conducted for all the ratings based on the type of respondents (PM or UM) to determine whether the population distribution was normal (Table 1). The results showed that all the population distribution of the dependent variables was not normal as the p value was less than .05.

Table 1.

Anderson–Darling Test (ADT) Results Regarding Satisfaction Levels With APD Benefits.

S. No.	Benefits of APD methods	Respondents type	ADT statistics	p value
1	Overall project delivery experience	Utility manager	3.96	<.01*
1	Overall project delivery experience	Project manager	6.75	<.01*
2	Quality of the completed project	Utility manager	3.54	<.01*
2	Quality of the completed project	Project manager	7.28	<.01*
3	Transition to operation	Utility manager	3.48	<.01*
3	Transition to operation	Project manager	5.32	<.01*
4	Risk distribution	Utility manager	3.93	<.01*
4	Risk distribution	Project manager	7.82	<.01*
5	Communication process	Utility manager	3.28	<.01*
5	Communication process	Project manager	5.78	<.01*
6	Owner’s involvement in design	Utility manager	7.76	<.01*
6	Owner’s involvement in design	Project manager	5.56	<.01*
7	Change orders	Utility manager	3.66	<.01*
7	Change orders	Project manager	5.29	<.01*
8	Lower claims and disputes than DBB projects	Utility manager	3.63	<.01*
8	Lower claims and disputes than DBB projects	Project manager	6.12	<.01*

Note. APD = alternative project delivery; DBB = design-bid-build; ADT = Anderson-Darling Test.

Significant at alpha level .05.

As the dependent variables were in ordinal scale and their population distributions were not normal, the Mann–Whitney U test was conducted to determine significant differences between the two groups—UMs and PMs—regarding their levels of satisfaction with the benefits of the APD methods. The results indicated a significant difference in satisfaction levels between UMs and PMs with regard to quality of the project completed, change orders, and claims and disputes (Table 2). Specifically, PMs were significantly more satisfied than UMs with the quality of projects when using APD methods (median rank 59 vs. 48), change orders (median rank 55 vs. 43), and claim and disputes (median rank 55 vs. 45). The satisfaction levels regarding other issues were not significantly different between PMs and UMs as their p values were greater than .05.

Table 2.

Mann–Whitney U Test Results for APD Benefits.

S. No.	Benefits of APD methods	Respondents type	No. of sample	Mean satisfaction level	Median rank	p value
1	Overall project delivery experience	Utility manager	47	4.2	52	.49
1	Overall project delivery experience	Project manager	60	4.3	56	.49
2	Quality of the completed project	Utility manager	46	4.2	48	.05*
2	Quality of the completed project	Project manager	61	4.5	59	.05*
3	Transition to operation	Utility manager	41	4.3	52	.51
3	Transition to operation	Project manager	58	4.2	49	.51
4	Risk distribution	Utility manager	39	4.0	49	.46
4	Risk distribution	Project manager	53	3.9	45	.46
5	Communication process	Utility manager	44	4.1	48	.27
5	Communication process	Project manager	59	4.3	55	.27
6	Owner’s involvement in design	Utility manager	44	4.5	52	.48
6	Owner’s involvement in design	Project manager	55	4.3	48	.48
7	Change orders	Utility manager	44	3.8	43	.03*
7	Change orders	Project manager	54	4.1	55	.03*
8	Lower claims and disputes than DBB projects	Utility manager	44	4.0	45	.05*
8	Lower claims and disputes than DBB projects	Project manager	56	4.3	55	.05*

Note. APD = alternative project delivery; DBB = design-bid-build.

Significant at alpha level .05.

Ranking of Reasons for Using APD Methods

The RIIs were calculated to determine the ranking of the reasons for using APD methods, based on responses from UMs and PMs, as shown in Table 3. Both groups ranked schedule first of the four top advantages, followed by quality, cost, and fewer disputes.

Table 3.

Ranking of Reasons for Using APD Methods.

S. No.	Reasons for using APD methods	Utility managers		Project managers
S. No.	Reasons for using APD methods	RII	Ranking	RII	Ranking
1	Schedule advantage	0.76	1	0.81	1
2	Better quality	0.68	2	0.72	2
3	Cost advantage	0.65	3	0.63	3
4	Fewer disputes	0.55	4	0.58	4

Note. APD = alternative project delivery; RII = Relative Importance Index.

The Anderson–Darling test was conducted to determine whether the population distribution of the dependent variables was normal based on the type of respondent. Results indicated that the population distributions of these variables were not normal as their p values were less than .05 (Table 4).

Table 4.

Anderson–Darling TestResults for Reasons for Using APD Methods.

S. No.	Reasons for using APD methods	Respondent types	ADT value	p value
1	Cost advantage	Utility manager	1.83	<.01*
1	Cost advantage	Project manager	2.81	<.01*
2	Schedule advantage	Utility manager	3.80	<.01*
2	Schedule advantage	Project manager	5.61	<.01*
3	Better quality	Utility manager	1.99	<.01*
3	Better quality	Project manager	2.76	<.01*
4	Fewer disputes	Utility manager	2.58	<.01*
4	Fewer disputes	Project manager	3.71	<.01*

Note. APD = alternative project delivery; ADT= Anderson-Darling Test.

Significant at alpha value .05.

The Mann–Whitney U test was conducted to determine significance differences between rankings given by UMs and PMs regarding the reasons for using APD methods. The results showed there was no significant difference observed between their responses as their p values were greater than .05 (Table 5). Both groups had similar reasons for using APD methods.

Table 5.

Results of the Mann–Whitney U Test.

S. No.	Reasons for using APD methods	Respondents type	No. of sample	Median rank	p value
1	Cost advantage	Utility manager	52	61	.49
1	Cost advantage	Project manager	64	57	.49
2	Schedule advantage	Utility manager	52	55	.35
2	Schedule advantage	Project manager	64	62	.35
3	Better quality	Utility manager	52	56	.42
3	Better quality	Project manager	64	61	.42
4	Fewer disputes	Utility manager	52	57	.45
4	Fewer disputes	Project manager	64	60	.45

Note. APD = alternative project delivery.

Comparison of Cost and Schedule Growth

The ANOVA test was conducted to determine the significant difference in cost growth and schedule growth of projects when using APD methods, based on each type of respondent. This test was conducted because the dependent variables were on a ratio scale. One of the major assumptions of the ANOVA test was that the population distribution of the dependent variables must be normal. This test was conducted to determine the normality of the population distribution. As shown in Table 6, the distribution of the dependent variables were normal because the p values of the test were greater than .05.

Table 6.

Anderson–Darling Test Results for Cost and Schedule Growth.

S. No.	Cost and schedule performance	Respondents type	ADT value	p value
1	Cost growth	Utility manager	0.32	.53
1	Cost growth	Project manager	0.55	.15
2	Schedule growth	Utility manager	0.60	.11
2	Schedule growth	Project manager	0.59	.12

Note. ADT= Anderson-Darling Test.

Another assumption of the ANOVA test was that the variances of the population distribution for both groups were equal. Therefore, Levene’s test was conducted to determine whether the samples had equal variances. The null hypothesis of this test was that the samples had equal variances. The null hypothesis would be accepted if the p value of the test was more than .05. Results showed that the p value of both cost growth and schedule growth were more than .05, confirming these dependent variables had equal variances (Table 7).

Table 7.

Levene’s Test for Homogeneity of Variance.

S. No.	Cost and schedule performance	Levene statistics	p value
1	Cost growth	0.49	.49
2	Schedule growth	0.05	.82

The ANOVA test was conducted to determine significant differences between cost growth and schedule growth as estimated by UMs and PMs (Table 8). The results showed that there was a significant difference between schedule growth as estimated by UMs and PMs. On average, UMs estimated that the projects were completed 1.38% behind the schedule, and PMs estimated that their projects were completed 1.15% ahead of the schedule. However, no significant differences in cost savings were estimated by UMs and PMs. PMs experienced cost savings of 0.16%, and UMs experienced cost growth by 0.54% in their projects.

Table 8.

ANOVA Test Results for Cost Growth and Schedule Growth.

S. No.	Cost and schedule performance	Respondents type	No. of sample	Mean (%)	p value
1	Cost growth	Utility manager	41	0.54	.62
1	Cost growth	Project manager	58	−0.16	.62
2	Schedule growth	Utility manager	34	1.38	.04*
2	Schedule growth	Project manager	52	−1.15	.04*

Significant at alpha level 0.05.

Results of Pearson’s Chi-Square Test

The respondents were asked whether they received any benefits when using APD methods in their water and wastewater projects. The responses of these questions were “yes” and “no.” Pearson’s chi-square test was conducted to determine differences between the responses of UMs and PMs (Table 9). The majority of UMs and PMs thought that APD projects allowed innovation. In addition, most respondents indicated that they saved time and money when using APD methods, and they were willing to reuse these methods again in another project. However, the p values for all four responses were higher than .05, confirming that the test failed to reject null hypothesis. Therefore, there was no significant difference in the responses provided by UMs and PMs. Furthermore, more respondents agreed that they saved time rather than money by using APD methods.

Table 9.

Pearson’s Chi-Square Test Results.

S. No.	Benefits of APD methods	Respondents type	Yes (%)	No (%)	Pearson’s Chi-Square value	p value
1	Innovation used	Utility manager	91	9	0.47	.49
1	Innovation used	Project manager	87	13	0.47	.49
2	Cost savings	Utility manager	61	39	1.51	.22
2	Cost savings	Project manager	72	28	1.51	.22
3	Time savings	Utility manager	84	16	0.29	.59
3	Time savings	Project manager	80	20	0.29	.59
4	Reuse of APD methods	Utility manager	87	13	1.43	.23
4	Reuse of APD methods	Project manager	94	6	1.43	.23

Note. APD = alternative project delivery.

Conclusions and Recommendations

The results of the survey revealed that the vast majority of UMs and PMs who have been involved with water/wastewater infrastructures using APD methods were highly satisfied with the quality of the completed projects, the level of owner’s involvement, communication among involved parties, innovative ideas used, the generation of fewer claims/change orders, and the smooth transition of constructed project to operation. In addition, the survey showed that the majority of respondents preferred a two-step procurement process and the “Best Value” method to select contractors. The main reason that owners used APD methods was for schedule advantages, followed by better quality and cost advantages.

When the responses were subdivided into two groups based on type of respondent, PMs were significantly more satisfied than UMs with APD benefits, particularly with regard to quality, change orders, and claims/disputes when compared with DBB projects. The satisfaction level regarding overall project delivery experience, transition to operation, risk distribution, communication process, and owner’s involvement in design were not found to be significantly different between these two groups. However, both of these groups were highly satisfied with these benefits. These benefits were related to management and procedural issues rather than technical issues. Therefore, both groups were equally satisfied with these benefits as compared with other benefits such as quality, change orders, and claims, which are more perceived by project-level personnel than management-level personnel.

Both UMs and PMs ranked schedule advantages, better quality, cost advantage, and fewer disputes as the reasons for using APD methods. UMs experienced schedule growth of 1.38% in their projects, whereas PMs experienced schedule savings of 1.15% in their projects. This difference was significant at alpha level .05. However, these two groups did not experience significant differences with regard to cost savings. Most respondents agreed that there are benefits of using APD methods in terms of cost saving, schedule advantage, and use of innovation; however, there was no significant difference between these two groups in the statistical results.

One of the important findings of this study was that project-level personnel experienced significantly higher benefits related to quality, change orders, claims, and schedule in APD methods than the management-level personnel. This indicates that the level of benefits perceived by PMs may be significantly different than UMs based on their position in the owners’ organizations. However, on average, the ratings of these benefits were higher than 4.0 on a scale of 5.0, which showed that both the management-level and project-level staffs were highly satisfied with the APD methods used in water and wastewater projects. The difference in the satisfaction ratings of these two groups of people could have significant consequences in choosing APD methods for the design and construction of water and wastewater infrastructures. If the management personnel of these projects (in this case, UMs) were more satisfied with benefits of APD methods than PMs, then it will be beneficial for the broader use of these APD methods. The use of APD methods cannot grow without satisfying the UMs, because they take the ownership of these projects after construction.

Given the many benefits and positive experiences reported, it is expected that the use of APD methods will increase significantly in the future to deliver high quality, innovative, timely, and cost-effective water/wastewater infrastructures. The authors had conducted various research related to use of APD methods in highways that shows similar results as this one. Therefore, if the satisfaction ratings of the owners are collected for other infrastructure projects, it is believed that the results will be similar, because the benefits related to time saving, quality, innovations, fewer change orders, and improved communication will be achieved using APD methods irrespective of the types of projects. The authors recommend that this type of longitudinal study be continued within a certain interval of time to determine the satisfaction level of owners regarding various issues relating to the use of APD methods in water and wastewater projects. In addition, detailed face-to-face interviews should be conducted as well as case studies of projects completed using APD methods should be studied to determine the advantages and disadvantages of these methods.

Footnotes

Acknowledgements

The authors thank Water Design Build Council for funding this survey project. They also thank The Cannon Survey Center at the University of Nevada, Las Vegas, for collecting data for the survey.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) declared the following financial support for the research, authorship, and/or publication of this article: Funding received from Water Design Build Council (WDBC) to conduct this study.

Author Biographies

Pramen P. Shrestha is an associate professor of construction engineering and management in Civil and Environmental Engineering and Construction Department of University of Nevada, Las Vegas. He has a PhD degree in civil engineering from University of Texas at Austin. He has been involved in research related to alternative delivery methods since 2004.

Ruiko Maharajan is a PhD student in Civil and Environmental Engineering and Construction Department of University of Nevada, Las Vegas. She is working on her dissertation to determine the benefits of use of alternative project delivery methods in water and wastewater projects.

Jacimaria R. Batista is a professor of water and environmental engineering in Civil and Environmental Engineering and Construction Department of University of Nevada, Las Vegas. She has a PhD in civil and environmental engineering from Pennsylvania State University. She has been involved in research related to water and wastewater since 2000.

Binita Shakya has completed her master in civil engineering from Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas. Her master thesis focused on the project performance of infrastructure projects constructed using design-build and construction manager at-risk.

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