Improving Sustainable Project Success Strategies Focused on Cost and Schedule for Electrical Construction Project Management

Abstract

Electrical contractors encounter problems such as limited construction sites, schedule interference, and inefficient communication with other contractors when they typically subcontract with general contractors. Electrical projects require effective and systematic project management strategies to overcome these problems and achieve the desired goal. In an electrical construction project, individual tasks are interconnected at different stages, including pre-construction planning (PCP) and project execution (PE). Therefore, analyzing the effect of task strings on the project success in terms of schedule and cost performance is necessary. The main objective of this study is to perform a static analysis to compare successful and failed projects with a focus on the cost and schedule performances, using the PCP and PE task strings in electrical construction projects. To achieve this, a continuous PCP-PE task strings implementation score was calculated for each PE group in terms of cost and schedule, and successful and failed projects on unweighted and weighted values were compared and analyzed by performing an independent sample t-test. Consequently, it was confirmed that the use of most task strings had a positive effect on the cost success at a confidence level of 95%, and that only the subcontractor management group had a positive effect on the schedule success. Hence, it was derived that the usage of task strings for these groups is recommended for cost success in electrical construction, and continuous PCP-PE task strings do not have a positive effect on schedule success; therefore, it is recommended to use the PCP-PE task strings only for specific groups for schedule success in electrical construction. Demonstrating the relationship between the PCP and PE tasks, the findings of this study are expected to help electrical contractors achieve a better performance using effective project management strategies.

1. Introduction

1.1. Background and Research Objectives

Effective execution of planned work is crucial to improving project performance in the construction industry [1]. Unreliable project management and execution increases waste and reduces productivity, thereby increasing the rate of failure in construction projects. In particular, poor planning and lack of control over construction execution requires rework that results in a large amount of lost productivity [2]. The conventional lean construction concept, which emerged in the construction industry in the mid-1990s, is applied to overcome these project management problems [3]. Lean construction is defined as a method of designing production systems to minimize time, material waste, and create maximum value [4]. That is, lean philosophy promotes the efficient use of resources [3,5,6,7,8,9,10] and highlights the reduction of wastes that ultimately have serious environmental effects [11,12,13,14,15].

Electrical contractors, as subcontractors, are exposed to disadvantages, such as schedule interference, limited workspace, and inefficient communication with other trades [16], which ultimately causes reduced productivity and schedule delays in electrical projects [17,18]. Therefore, electrical contractors need more systematic and effective project management strategies to solve these problems and achieve the desired project goals efficiently. Many studies have been conducted to analyze various management aspects of electrical construction projects. Studies include the analysis of the effect of change orders on electrical contractors [19,20], scheduling [21], and design/construction project considerations [22], productivity benchmarking [23], electrical worker safety [24], absenteeism factors of electricians [25], conceptual planning process [26], coordination of mechanical, electrical, and plumbing trades [18,27,28], using building information modeling by electrical contractors [29,30], human resource recruitment and detainment strategies [31], and prefabrication in electrical construction [32]. However, although many studies have been conducted on electrical construction projects, management strategies have not been investigated sufficiently to successfully implement them into projects.

Project management became an issue to be considered at the organizational level [33], and this need led to the concept of organizational project management maturity in the late 1990s [34]. Many project maturity models have been introduced and related studies have been conducted. Iqbal (2013) provided an overview of the existing project maturity model [35], and Yazici (2009) showed that the contribution of project management maturity to better project performance is largely determined by organizational culture characteristics [36]. In particular, the International Journal of Managing Projects in Business published a special issue in 2014 on the topic of project management maturity. In addition, Pasian (2014) pointed out the decisive role of non-process factors in achieving mature project management for projects that are not precisely defined [37]. In addition, studies on more successful project management have been conducted using the project management maturity model [38,39,40,41]. Therefore, it is necessary to quantify and analyze the degree of management to successfully carry out the project in the field of electrical construction.

In electrical construction projects, individual tasks of pre-construction planning (PCP) and project execution (PE) are correlated with each other at different stages [16]. Relevant tasks in PCP-PE must be linked and completed consecutively to achieve an efficient PE, instead of completing PCP or PE tasks separately. Therefore, a continuous task string model was developed which consists of specific combinations of the PCP and PE tasks [1]. The PCP-PE task string model can be utilized to recognize the task relationship between planning and execution. It can eliminate unnecessary tasks and improve project productivity by efficiently using PCP-PE task strings. However, to verify the effect on PCP-PE task strings, it is necessary to investigate its impact on project performance. In particular, the effectiveness of continuous task strings on project performance must be quantified in terms of the schedule and cost success [42], which are significant in construction projects. The main objective of this study is performing the static analysis to compare successful and failed projects with a focus on the cost and schedule, using task strings of PCP and PE in electrical constructions. Meeting project cost and schedule is an important factor contributing to project success. Therefore, this paper is limited to success in terms of cost and schedule.

1.2. Research Methodology

This study involved the following steps:

(1)

Review the existing studies on PCP and PE, and the concept of PCP-PE task strings.

(2)

Collect data of 50 recently completed projects from 25 electric companies in the United States.

(3)

Evaluate the importance of cost and schedule success for 239 continuous task strings by 29 electrical experts.

(4)

Introduce methods for applying the unweighted/weighted value to quantify the performance of task strings implementation.

(5)

Develop hypotheses regarding the mean of successful and failed projects to statistically analyze the effect of task strings.

(6)

Calculate continuous PCP-PE task strings implementation score for each PE group in terms of cost and schedule.

(7)

Compare and analyze successful and failed projects on unweighted and weighted values by performing an independent sample t-test.

2. Preliminary Study

2.1. Existing Studies of PCP and PE

2.1.1. PCP

Project planning was included in some textbooks as part of the project control and management process in the 1970s and early 1980s [43,44]. In the late 1980s and early 1990s, several independent studies were conducted on construction project planning [45,46]. Furthermore, Gigado (2004) presented a systematic approach for planning that could improve and standardize the PCP process of prime contractors [47]. The proposed model provides a best practice approach to the PCP processes of prime contractors. Since the late 1990s, there has been relatively few studies on planning; however, advanced formalized plans are needed to maintain competitiveness in an increasingly dynamic construction industry [48].

Menches and Hanna (2006) proposed a model PCP process that included 123 planning tasks performed by electrical contractors using the data collected. Menches (2006) statistically compared the differences in the implementation of planning tasks between successful and failed projects to complement the baseline planning process. Consequently, 46 critical tasks consistently performed on successful projects, were selected for inclusion in the PCP process. These tasks were classified into ten categories: (1) team selection and turnover, (2) administrative setup, (3) scope and contract review, (4) material-handling plan, (5) buyout process, (6) layout and sequencing plan, (7) budget preparation, (8) tracking and control, (9) schedule development, and (10) construction-execution kickoff meeting. Kim et al. (2013) investigated the frequency of the execution of these tasks and achieved cost and schedule successes. Thus, five groups were excluded, and eleven PCP tasks in only five groups are listed in

Table 1. Example list of PE groups and tasks.

PCP Group No.	PCP Group	PCP No.	PCP Task
1	Team Selection & Turnover	2	Hold turnover meeting between project manager and estimator
		3	Hold separate turnover meeting between field supervisor and project manager
2	Scope & Contract Review	7	Review plans, schedule, and specifications (Field supervisor)
		9	Conduct site visit
		10	Compare estimated materials and work activities to planned performance
		11	Identify value engineering and prefabrication opportunities and how to simplify the tasks
		12	Prepare construction takeoff
3	Budget Preparation	27	Develop, review, or expand cost code schemes
4	Layout & Sequencing Plan	30	Develop layout drawings and installation sequence
		31	Develop including panel, field instructions, pull, or conduit schedules
5	Tracking & Control	38	Customize the control system and computerized tracking, i.e., schedule/database, and so on, for the current project

.

2.1.2. PE

Nasr (2009) developed an electrical PE process using 85 tasks by constructing a model using tasks performed on successful projects [49]. The tasks were classified into the following 14 categories: (1) mobilization, (2) document management, (3) material management, (4) tool management, (5) subcontractor management, (6) safety, (7) communication, (8) coordination, (9) scope and change control, (10) scheduling, (11) cost control and billing, (12) quality management, (13) labor management, and (14) project closeout. Kim et al. (2015) investigated the achieved cost and schedule. Therefore, only the coordination group was excluded, and 47 PE tasks in 13 groups are listed in

Table 2. List of PE groups and tasks.

PE Group No.	PE Group	PE No.	PE Task
1	Team Selection & Turnover	1	Setup office trailer and in a convenient location in a timely manner
		2	Setup and lay down storage trailer area
		7	Make sure the foreman has everything he or she needs to get started
2	Document Management	8	Make use of a project file
		9	Use a documentation control system
		10	Use an RFI processing and tracking system
		11	Use a change order processing and tracking system
		12	Keep all schedule documents including delays
3	Material Management	17	Review of material and supplier bid documents
		19	Establish delivery dates
		22	Ensure good material handling on site
		23	Communicate all material information to the field
		25	Lock in the necessary prices
		27	Make sure the invoice matches the material costs
4	Tool Management	28	Review contract drawing, specifications, and bid
		29	Schedule deliveries and pickups
5	Subcontractors Management	31	Determine the scope of work for the subcontractors
		32	Establish subcontracts
		33	Determine the subcontractors’ schedule
		34	Request submittals and shop drawings
		35	Inform the site about the subcontractors
		37	Ensure the subcontractors are capable of doing the job
6	Safety Management	39	Identify safety issues with the existing work
		40	Plan for additional needs for safety equipment
		42	Perform work walks to ensure the safety rules
7	Communication	43	Receive support from the company Chief Executive Officer or Vice President
		44	Communicate with the foreman
		45	Communicate with the subcontractors and vendors
		46	Communicate with the owner and general contractor
8	Scope & Change Control	54	Track change orders
9	Scheduling	57	Identify milestone dates and review the schedule
		58	Identify work that impacts electrical activity
		60	Review the schedule with the site
		61	Update the work schedule regularly
10	Cost Control & Billing	63	Use cost codes, i.e., cost breakdown
		64	Track labor costs
		65	Track subcontractor and material costs
		66	Include issued change orders
		68	Compare the project costs to the budget
11	Quality Management	72	Check the quality that the site needs
		73	Check the installation quality through on-site visits
		74	Perform test results/commissioning
12	Labor Management	77	Maintain the correct staff power and crew mix level
		78	Ensure labor hours are turned in
13	Project Closeout	79	Ensure that all punch list items are signed off on
		82	Ensure that all purchase/change orders are closed
		84	Turn all project closeout documents over to the general contractor

.

2.2. Concept of Task String

From the contractor’s perspective, after engineering and design completion, construction projects typically progress through the stages of bidding, PCP, PE, and commissioning (or closing) [48]. PCP tasks are associated with PE tasks because PCP tasks are used to plan and manage various systems to efficiently manage PE tasks. Therefore, related planning and execution tasks must be executed sequentially in line with the project phases to achieve effective project management. A combination of planning and execution tasks is defined as task strings because it must start with PCP and progress continuously through the completion of PE.

For example, in the PCP phase, material handling plans involve the process of ordering, receiving, preparing, and storing major equipment and materials at the site. These planning tasks relate to material management tasks performed during the PE phase and include receiving material deliveries, checking packaging for damage, comparing deliveries against invoices, and storing materials according to the storage plan. Therefore, material handling plans should be derived during the PCP phase to effectively implement material management tasks during the PE phase. In this case, the two tasks are called continuous task strings between the PCP and PE phases [1]. Kim et al. (2015) provided the results of modeling continuous task strings and examining their effects on project performance. This study introduces a new concept called continuous task strings consisting of particular combinations of PCP and PE tasks that are expected to improve project performance. In addition, 3910 combination tasks of the PCP (46 tasks) and PE (85 tasks) phases were reduced to 239 task strings, as shown in

Table 3. Example list of PCP task-to-PE task.

Task Strings No.	PCP No.	PCP Task	PE Group
1	3	Hold separate turnover meeting between field supervisor and project manager	Mobilization
2	7	Review specifications, plans, and schedule (Field supervisor)
3	9	Conduct site visits
4	11	Identify prefabrication opportunities, value engineering and how to simplify the work
5	3	Hold separate turnover meeting between field supervisor and project manager
236	3	Hold separate turnover meeting between field supervisor and project manager	Project Closeout
237	38	Customize the computerized control and tracking system (database/schedule, etc.) for the current project
238	3	Hold separate turnover meeting between field supervisor and project manager
239	38	Customize the computerized control and tracking system (database/schedule, etc.) for the current project

. However, the effect of continuous PCP-PE task strings on project performance has not been explored and verified adequately.

3. Continuous PCP-PE Task Strings Performance Analysis Plan for Project Success

3.1. Data Collection

Data from 50 completed projects were collected from 25 electric companies in the United States to perform PCP-PE task string analysis. They were randomly selected from a list of 2000 electrical members of the National Electrical Contractors Association. Each company provided data on successful and failed projects. The data consisted of information on PCP-PE task execution and project performance, categorized as success or failure in terms of schedule and cost. A project was classified as a failed project if the target cost and schedule were not met; otherwise, it was classified as a successful project. In terms of cost, 30 of 50 electricity projects were classified as successful and 20 as failed, whereas 42 and 8 projects were classified as successful and failed, respectively, considering the schedule criterion. That is, in terms of schedule and cost, success and failure were classified according to the degree of achievement, and the degree was investigated through a survey. To quantify the performance of implementing 239 continuous PCP-PE task strings to project success, electrical experts prioritized their importance considering the cost and schedule success criteria. The survey was sent via email to 75 electricians, and 29 surveys were collected, yielding a response rate of 38.7%.

3.2. Measurement Method of Implementing Continuous PCP-PE Task Strings

3.2.1. Implementation of Unweighted Task Strings

The continuous PCP-PE task string performance score measures the level of performance for electrical projects. The task strings were grouped into 13 PCP tasks versus PE groups according to the PE task category. Thus, the PCP-PE task string implementation score quantified the extent to which task strings were implemented within the PCP-task-to-PE group during the PCP and PE stages. For simplicity, the task strings were weighted uniformly to avoid bias, and the mean score was in the range of 0–1. A mean value of 1 indicates that all task strings within each PCP-task-to-PE- group have been implemented for the project (success or failure project), whereas a mean value of 0 indicates that no task string has been implemented. The PCP-PE task string implementation score is calculated as Equation (1):

$${\mathrm{UTS}}_{\mathrm{IS}}={\displaystyle \sum }_{i=1}^n ({\mathrm{TS}}_i)/\mathrm{N},$$

(1)

where UTS_IS is the unweighted task string implementation score, TS_i represents the i-th task string performance, and N is the number of task strings on PCP task-to-PE group.

3.2.2. Weighted Task Strings Implementation

The weights of each task string were also applied to reflect their relative importance, and the continuous PCP-PE task strings implementation score was calculated using Equation (2); five to seven electrical experts rated the importance of each task string to achieve schedule and cost success on a Likert scale of 0 to 4 (0 = not important, 4 = very important). The raw score was converted to a 0–10 score by multiplying it by 2.5 to make the relative importance clearer. These transformed cost and schedule importance mean scores were used as weight values for the PCP-PE task strings.

$${\mathrm{WTS}}_{\mathrm{IS}}={\displaystyle \sum }_{i=1}^n{\displaystyle \sum }_{j=1}^m ({\mathrm{TS}}_i{\mathrm{W}}_j\times 2.5)/\mathrm{N},$$

(2)

where WTS_IS represents the weighted task string implementation score, TS_i is the i-th task string implementation, W_j represents the j-th relative importance of the task string, and N is the number of task strings on PCP task-to-PE group.

3.3. Hypothesis Setting

An independent samples t-test was performed considering the cost and schedule success to determine the differences in the work string usage. This analysis was conducted to statistically determine any significant differences between the projects with successes and failures in task string implementation. An independent sample t-test requires measuring dependent variables on a ratio scale or an interval, whereas the independence, normality, and equal variance must be satisfied. The independent variables are successful and failed projects in terms of the schedule and cost, and the dependent variables are the continuous PCP-PE work string implementation score means. In addition, Levene’s test was used as an inferential statistic test to assess the equality of population variances. In this study, the effect of work strings on project performance was analyzed by dividing it into the cost and schedule criteria. The continuous PCP-PE task string implementation score means for successful and failed projects for 13 PE task category groups were comparatively analyzed by dividing them into unweighted and weighted PCP-PE task strings. Figure 1 shows that the four analysis results are derived in terms of the cost and schedule performances. The null Hypothesis (H1) is ‘The PCP-PE task strings implementation score means for successful and failed projects are the same’ and the alternative Hypothesis (H2) is ‘The PCP-PE task strings implementation score means for successful and failed projects are different.’ This was verified at a confidence level of 95%.

Hypothesis 1 (H1).

The PCP-PE work string implementation score means for successful and failed projects are the same.

Hypothesis 2 (H2).

The PCP-PE work string implementation score means for successful and failed projects are different.

4. Performance Analysis of the Effect of Work Strings on Cost and Schedule

4.1. Analysis of Cost Success and Failure

4.1.1. Effect of Unweighted Task Strings

The continuous PCP-PE work string implementation score was calculated as an unweighted task string in terms of cost. Figure 2 presents the mean PCP-PE task string implementation scores for successful and failed projects. The mean of the successful projects is always higher than that of the failed projects; thus, a correlation exists between the obtained means. However, because this is just a comparison of the mean values, the distribution of the overall values is difficult to analyze.

Table 4. Effect of unweighted task strings usage on cost success.

Group No.	PCP Task-To-PE Group	Success Projects			Failure Projects			Mean Difference	t Statistic	Sig. (Two-Tailed)	Eta Square
		N	M	SD	N	M	SD
1	Mobilization	30	0.73	0.04	20	0.45	0.06	0.28	4.23	0.000	0.272
2	Document Management	30	0.63	0.26	20	0.42	0.27	0.21	2.81	0.007	0.141
3	Material Management	30	0.62	0.24	20	0.36	0.24	0.26	3.73	0.001	0.225
4	Tool Management	30	0.67	0.28	20	0.41	0.33	0.26	2.92	0.005	0.151
5	Subcontractor Management	30	0.59	0.29	20	0.49	0.34	0.10	–	–	–
6	Safety Management	30	0.57	0.37	20	0.39	0.36	0.18	–	–	–
7	Communication	30	0.58	0.32	20	0.35	0.34	0.23	2.45	0.018	0.111
8	Scope Change & Control	30	0.46	0.37	20	0.38	0.39	0.08	–	–	–
9	Scheduling	30	0.57	0.36	20	0.24	0.28	0.33	3.44	0.001	0.198
10	Cost Control & Billing	30	0.57	0.23	20	0.31	0.26	0.26	3.78	0.000	0.229
11	Quality Management	30	0.63	0.29	20	0.45	0.33	0.18	2.05	0.046	0.081
12	Labor Management	30	0.59	0.32	20	0.17	0.27	0.42	4.88	0.000	0.332
13	Project Closeout	30	0.68	0.27	20	0.49	0.30	0.19	2.39	0.021	0.106

shows the results of the independent sample t-test for cost performance to explore the differences in task string usage, indicating that many PCP-task-to-PE groups significantly differ between successful and failed projects. As the analysis was performed at a confidence level of 95%, the p-value was not greater than 0.05, the null hypothesis was rejected, and the alternative hypothesis was accepted. That is, the difference between the mean values indicates that successful projects have a higher probability of success owing to the use of task strings than that of the failed projects, including the mobilization, document management, material management, tool management, communication, cost control and billing, scheduling, labor management, quality management, and project closeout. However, the other groups did not exhibit significant results statistically. Thus, with the exception of the subcontractor management, scope change, safety management, and control, which did not produce statistically significant results, task-string groups are predicted to contribute considerably to cost success.

4.1.2. Effect of Weighted Task Strings

Figure 3 presents the results obtained considering a weighted continuous PCP-PE task string implementation score in terms of successful and failed projects. Similar to the unweighted task strings, the mean of successful projects is always higher than that of failed projects; thus, a correlation exists between the mean values.

Table 5. Effect of weighted task strings usage on cost success.

Group No.	PCP Task-To-PE Group	Success Projects			Failure Projects			Mean Difference	t Statistic	Sig. (Two-Tailed)	Eta Square
		N	M	SD	N	M	SD
1	Mobilization	30	3.77	1.10	20	2.33	1.32	1.43	4.18	0.000	0.266
2	Document Management	30	3.54	1.45	20	2.34	1.49	1.20	2.84	0.007	0.144
3	Material Management	30	3.53	1.39	20	2.04	1.38	1.49	3.74	0.000	0.226
4	Tool Management	30	3.88	1.68	20	2.40	1.92	1.48	2.89	0.006	0.148
5	Subcontractor Management	30	3.34	1.65	20	2.76	1.94	0.58	–	–	–
6	Safety Management	30	3.27	2.12	20	2.24	2.06	1.03	–	–	–
7	Communication	30	2.98	1.68	20	1.70	1.77	1.28	2.58	0.013	0.122
8	Scope Change & Control	30	2.68	2.19	20	2.27	2.35	0.41	–	–	–
9	Scheduling	30	3.43	2.15	20	1.41	1.68	2.02	3.54	0.001	0.207
10	Cost Control & Billing	30	3.24	1.33	20	1.73	1.47	1.51	3.78	0.000	0.229
11	Quality Management	30	3.17	1.49	20	2.23	1.66	0.94	2.10	0.041	0.084
12	Labor Management	30	3.46	1.85	20	0.98	1.59	2.48	4.92	0.000	0.335
13	Project Closeout	30	3.49	1.48	20	2.44	1.56	1.05	2.40	0.020	0.107

shows the results of an independent sample t-test for cost performance. Additionally, an independent sample t-test was performed to investigate the differences in the work string usage. As the p-value is not greater than 0.05, the alternative hypothesis is accepted. That is, many work-string groups significantly differed between successful and failed projects at a confidence level of 95%. These results are consistent with those of the unweighted work-string groups. However, the three task-string groups (subcontractor management, scope change & control, and safety management) did not produce statistically significant results, which indicates that the effectiveness of the strings in the PE phase is difficult in these three groups. Therefore, the task strings are predicted to have a positive effect on the cost success; thus, the use of task strings for cost success in electrical construction projects is recommended.

4.2. Analysis of Schedule Success and Failure

4.2.1. Effect of Unweighted Task Strings

Figure 4 demonstrates the continuous PCP-PE task strings implementation mean score for unweighted work strings in terms of successful and failed projects considering the schedule performance. Except for the communication group, all of the means of the successful projects are always higher than those of the failed projects.

Table 6. Effect of unweighted work strings usage on schedule success.

Group No.	PCP Task-To-PE Group	Success Projects			Failure Projects			Mean Difference	t Statistic	Sig. (Two-Tailed)	Eta Square
		N	M	SD	N	M	SD
1	Mobilization	42	0.63	0.27	8	0.54	0.23	0.09	–	–	–
2	Document Management	42	0.56	0.29	8	0.50	0.24	0.06	–	–	–
3	Material Management	42	0.54	0.27	8	0.39	0.22	0.15	–	–	–
4	Tool Management	42	0.60	0.32	8	0.36	0.28	0.24	1.96	0.055	0.074
5	Subcontractor Management	42	0.59	0.29	8	0.34	0.34	0.25	2.16	0.036	0.089
6	Safety Management	42	0.54	0.37	8	0.29	0.35	0.26	1.81	0.076	0.064
7	Communication	42	0.48	0.35	8	0.56	0.35	-0.09	–	–	–
8	Scope Change & Control	42	0.46	0.37	8	0.25	0.39	0.21	–	–	–
9	Scheduling	42	0.47	0.38	8	0.29	0.27	0.18	–	–	–
10	Cost Control & Billing	42	0.48	0.27	8	0.36	0.26	0.12	–	–	–
11	Quality Management	42	0.58	0.32	8	0.44	0.30	0.14	–	–	–
12	Labor Management	42	0.45	0.37	8	0.28	0.32	0.18	–	–	–
13	Project Closeout	42	0.62	0.29	8	0.53	0.35	0.09	–	–	–

describes the results of an independent sample t-test for schedule performance to investigate the differences in the task strings usage. The PCP-PE task strings implementation score includes the mean of successful and failed projects considering the schedule performance. Only the p-value of subcontractor management was 0.036 when the analysis was at a confidence level of 95%. Thus, the null and alternative hypothesis were rejected and accepted, respectively, indicating that the influence of work strings usage within the group differed between the successful and failed projects. Consequently, using task strings is predicted to increase the probability of project success.

However, ten different task-string groups, including mobilization, document management, material management, scope change and control, communication, scheduling, cost control and billing, labor management, quality management, and project closeout, did not produce statistically significant results. The p-values of tool and safety management were 0.055 and 0.076, respectively, indicating a significant difference between the successful and failed projects at a confidence level of 90%. That is, the three task-string groups (tool management, safety management, and subcontractor management) can contribute significantly to schedule success at a confidence level of 90%.

4.2.2. Effect of Weighted Task Strings

Figure 5 shows the mean of continuous PCP-PE task-string implementation scores for successful and failed projects concerning the schedule performance. Unlike Figure 4, all of the mean values of the successful projects, including the communication group, are always higher than those of the failed projects. Table 6 lists the results of an independent sample t-test concerning the schedule performance. The p-value of subcontractor management was 0.034, which rejected the null hypothesis. Therefore, the effect of task-string usage was predicted to differ significantly between the successful and failed projects at a confidence level of 95% concerning the schedule performance.

However, in

Table 7. Effect of weighted task strings usage on schedule success.

Group No.	PCP Task-To-PE Group	Success Projects			Failure Projects			Mean Difference	t Statistic	Sig. (Two-Tailed)	Eta Square
		N	M	SD	N	M	SD
1	Mobilization	42	3.64	1.56	8	3.13	1.35	0.51	–	–	–
2	Document Management	42	3.36	1.75	8	3.02	1.48	0.34	–	–	–
3	Material Management	42	3.32	1.67	8	2.37	1.34	0.95	–	–	–
4	Tool Management	42	3.83	2.06	8	2.30	1.79	1.53	1.97	0.055	0.075
5	Subcontractor Management	42	3.70	1.85	8	2.10	2.15	1.60	2.19	0.034	0.091
6	Safety Management	42	2.99	2.03	8	1.57	1.98	1.42	1.82	0.075	0.065
7	Communication	42	2.43	1.83	8	2.94	1.83	-0.51	–	–	–
8	Scope Change & Control	42	2.63	2.11	8	1.44	2.21	1.19	–	–	–
9	Scheduling	42	2.98	2.41	8	1.85	1.68	1.13	–	–	–
10	Cost Control & Billing	42	2.53	1.48	8	1.88	1.31	0.65	–	–	–
11	Quality Management	42	3.02	1.67	8	2.34	1.57	0.68	–	–	–
12	Labor Management	42	2.72	2.24	8	1.69	1.94	1.03	–	–	–
13	Project Closeout	42	3.30	1.65	8	2.90	1.96	0.40	–	–	–

, 10 task strings groups, including mobilization, communication, document management, material management, scope change and control, scheduling, quality management, cost control and billing, project closeout, and labor management, did not produce statistically significant results. In addition, p-values of tool management and safety management were 0.055 and 0.075, respectively, showing a significant difference between the success and failure projects at the 90% confidence level. Therefore, this means that most of the task strings do not have a positive effect on the schedule success.

In Figure 5, mean values of the successful projects were always higher than those of the failed projects; thus, it seemed that there was a correlation between the mean values. However, the statistical analysis in Table 6 demonstrates significantly different results between the correlated mean values. This is because the mean value is different for each distribution; therefore, it cannot be clearly determined that the successful projects contributed more positively than the failed projects using PCP-PE task strings. In addition, the results illustrate that cost had a positive effect on the use of task strings compared to schedules. Consequently, it is recommended to use task strings only in certain groups for achieving schedule success in electrical construction projects.

5. Validation of Continuous PCP-PE Task Strings

To validate that continuous PCP-PE work strings contribute to project success, the impact of task strings implementation on project success was investigated in terms of cost and schedule performance. The basic concept of validation is to ensure that the implementation of continuous PCP-PE work strings contribute to the success of the project. For verification, 50 sets of electrical projects were divided into two groups: successful and failed projects. In total, 30 of the 50 electricity projects were classified as cost-successful and 42 on schedule. Furthermore, the impact of the task strings’ performance on project success was evaluated to identify valuable work strings. The following four hypotheses were developed to identify worthwhile work strings:

Hypothesis 3 (H3).

Work strings are critical to project success when performed frequently in successful projects.

Hypothesis 4 (H4).

Work strings are less critical to project success when performed less frequently in successful projects.

Hypothesis 5 (H5).

Work strings do not contribute to project success when performed frequently in failed projects.

Hypothesis 6 (H6).

Work strings are required for project success when performed less frequently in failed projects.

Hypotheses H1 and H4 showed that several task strings contribute to project success. However, H2 and H3 confirmed that some task strings do not contribute to project success. Thus, the effectiveness of work strings is validated when they are implemented frequently in successful projects and less frequently in failed projects. Figure 6 shows the validation process concept.

As shown in Figure 6, work strings implemented in more than 50% of successful projects (A) and less than 50% of failed projects (B) were confirmed to have contributed to project success. On the other hand, task strings implemented in less than 50% of successful projects (C) or implemented in more than 50% of failed projects (D) were invalidated. For validation, 239 work strings ranked in the Best/Better/Basic work string categories were analyzed in terms of schedule and cost performance. Based on the percentage of task string performance in successful and failed projects, each task string was constructed to identify the categories it should contain. Figure 7 graphically shows plots of each task string for successful and failed projects. On the 239 continuous work strings, those included in both A and B were identified as task strings contributing to project success. Therefore, the impact of 196 action strings on cost success and the impact of 145 task strings on schedule success was determined. The verification results are described in

Table 8. Validation results.

Project Performance	Ranking of Task Strings	Original Task Strings No.	Validated Task Strings No.	Validated Task Strings (%)
Cost Success	Basic	186	116	62.4
	Better	48	41	85.4
	Best	5	3	60.9
Schedule Success	Basic	233	133	57.1
	Better	5	5	100.0
	Best	1	1	100.0

.

In total 66.9% of ranked task strings were validated for cost success and 74.1% for schedule success. In particular, better work strings were identified more than other rank strings, especially with a cost success rate of 85.4% and a schedule success rate of 100%. However, a relatively small number of basic task strings were validated in terms of their contribution to project success, representing 62.4% in cost success and 57.1% in schedule success, respectively. The validation of the effectiveness of work strings was confirmed in terms of cost and schedule success. In other words, more than 67% of task strings were found to contribute to project success. A list of high-value work strings (best task strings) is summarized in

Table 9. Validated best task strings for cost and schedule.

Project Performance	Task Strings No.	PCP No.	PCP Tasks	PE No.	PE Tasks
Cost Success	5	3	Hold separate turnover meeting between project manager and field supervisor	2	Setup storage trailer and lay down area
	56	11	Identify value engineering and prefabrication opportunities and how to simplify the work	17	Review bid documents for materials and vendors and any vendor responsibilities
	171	11	Identify value engineering and prefabrication opportunities and how to simplify the work	58	Identify work that impacts electrical activity
Schedule Success	126	7	Review plans, specifications, and schedule (Field supervisor)	33	Determine the subcontractors’ schedule

.

6. Discussion

To quantitatively evaluate the subjective project maturity, this study analyzed whether continuous PCP-PE task strings affect project success in terms of cost and schedule through a survey of electricians. Project maturity refers to an organization’s ability to master a project [50]. Companies with high project maturity are considered more likely to succeed than companies with low project maturity. Measuring project maturity can be subjective rather than objective [51]. Some of the most important work on project maturity focuses primarily on the operations performed by organizations and project people [52]. In addition, projects either fail or succeed for no particular reason. In other words, a project does not fail or succeed on one variable, but is influenced by many other factors.

Mullaly (2014) emphasized the need to consider both organizational and contextual factors and pointed out the need for a conditional perspective on project maturity assessment both in the project process and the context [53]. Furthermore, it was considered that the situational factor was underestimated because the maturity model has a repeatable process and the process itself is an appropriate tool to improve the project management maturity. Many studies have suggested the need to identify organizational-level determinants to achieve higher levels of maturity [39,54,55,56,57]. In addition, some studies have emphasized that they do not consider organizational-level determinants and other contextual factors that shape organizational project management maturity [58,59].

7. Conclusions

A continuous task-string model was developed by linking the PCP and related PE tasks to improve the performance of electrical construction projects. In this study, statistical analyses were performed to test the effect of 13 categories on project success using continuous PCP-PE task strings. Independent sample t-tests were performed using 50 completed projects to compare the successful and failed projects concerning the cost and schedule performance. The conclusions are drawn as follows:

First, it was confirmed that the use of most task-string groups had a positive effect on the cost success at a confidence level of 95%. Positive results were obtained for unweighted and weighted task strings in 10 of the 13 categories: mobilization, document management, material management, tool management, communication, scheduling, cost control and billing, quality management, labor management, and project closeout. The use of task strings for these groups is recommended for achieving cost success in electrical construction projects.

Second, it was confirmed that the use of continuous PCP-PE task strings had a positive effect on schedule success only in the subcontractor management group at a confidence level of 95%. Among the other categories, it was confirmed that the tool and safety management groups had a positive relationship at only a confidence level of 90%. A total of ten PCP-PE task-string groups, including mobilization, document management, material management, communication, scope change and control, scheduling, cost management and billing, quality management, labor management, and project closeout, did not produce statistically significant results for project success. Therefore, continuous PCP-PE task strings do not have a positive effect on the schedule success, and it is recommended to use PCP-PE task strings only for certain groups to achieve schedule success in electrical construction projects.

Third, the mean values of task-string score for successful projects were approximately higher than those of the failed projects; thus, it seemed that the mean values differed. However, statistical analysis demonstrated that there were no significant differences between the successful and failed projects in several categories. Therefore, the mean value of successful projects is not always higher than that of failed projects in the distribution of actual mean values. In the case of groups with a mean value that did not produce a statistically significant result, the project cannot always succeed even if task stings are used.

In this study, it was confirmed that continuous PCP-PC task strings can be applied to electricity projects to improve cost and schedule performances. This study provides empirical evidence to support the possibility that the introduced task string implementation can significantly help electricity construction projects. By leveraging the developed task strings to help contractors fully understand the relationship between certain tasks and beneficial outcomes, they can be used as task-level strategies that might lead to project success. In addition, it can contribute to research on the establishment of management strategies to implement projects successfully in the field of electrical construction. This study was conducted on general projects; however, in future research, additional task strings that have a considerable effect on the project characteristics should be identified for the expansion of continuous PCP-PE task strings.