Exploring the links between task-level automation usage.pdfAbstract Hybrid assembly systems consist of an automatic assembly section that feeds a manual finishing line. They are competitive

8/14/2019 Exploring the links between task-level automation usage.pdfAbstract Hybrid assembly systems consist of an automatic assembly section that feeds a manual fi

1/9

Exploring the links between task-level automation usage

and project satisfaction

Li-Ren Yang

Department of Business Administration, Tamkang Uni versity, Tamsui, Taipei, 251, Taiwan

Accepted 7 August 2007

Abstract

The purpose of this study was to identify project satisfaction-leveraging tasks and common characteristics associated with these critical tasks.

To address the primary aim, a survey was conducted to determine correlations between task-level automation adoption and project satisfaction

from the perspectives of various types of project stakeholders. Identification of project satisfaction-leveraging tasks is employed as a way to gain

greater understanding of the connections. Also, this study explores the links between automation utilization and project satisfaction in further

detail. Task characteristics are investigated as an additional basis for gaining deeper insights into how automation technology usage may impact

project satisfaction. A second survey was used to collect needed data from industry professionals. The analyses suggest that degrees of automation

used in executing the project satisfaction-leveraging tasks may be positively related to project stakeholder satisfaction. The results also indicate

that information and data intensive and management-related characteristics may be associated with overall project stakeholder satisfaction.

2007 Elsevier B.V. All rights reserved.

Keywords: Automation; Technology; Project; Stakeholder satisfaction; Task

1. Introduction

Many studies have shown that the construction industry is

reluctant to apply new technologies and employs lower levels of

technology than other industries. A national-wide survey

conducted by the Civil Engineering Research Foundation

indicated that the design and construction industry spends

only 0.5% of its total revenues on research and development[1].

In recent years, however, there has been a growing trend

towards increased technology utilization levels on Architect/

Engineering/Construction (A/E/C) capital facility projects.According to a 1995 study, four drivers for adoption of new

technologies were identified: 1) competitive advantage, 2)

external requirements, 3) priority problems avoid losses

from reduced performance, and 4) technological opportunity to

improve operations [2]. Some construction firms adopt

automation in the attempt to reduce the cost and schedule of a

project. These companies are also examining their operations

for ways to improve stakeholder satisfaction. However, since

the benefits of innovation can be rather intangible, this has

slowed or prevented the adoption of new automation technol-

ogy. Accordingly the impact of automation on project

performance has been one of the major issues for both industry

and academic fields. In order to understand the benefits, there is

a need for quantification of the benefits derived from

automation application. Simmons [3] suggested that methods

used to evaluate the impact of technology on performance

measures may be the problem in connecting investment in

technology to improvements in business performance. Researchon automation utilization at the task level and its associations

with project success should offer tangible evidence of

advantages from using technologies.

While many studies have promoted technology as a means to

enhance project performance [47], very few published

empirical studies in construction have explored the direct

effects of automation on project performance from the

perspectives of various types of stakeholders. None of the

previous research attempts to explain the links between

automation usage and project performance. In addition, there

has been no comprehensive study on the impacts of automation

Automation in Construction 17 (2008) 450458

www.elsevier.com/locate/autcon

Tel.: +886 2 26215656; fax: +886 2 26209742.

E-mail address:[email protected].

0926-5805/$ - see front matter 2007 Elsevier B.V. All rights reserved.doi:10.1016/j.autcon.2007.08.001
mailto:[email protected]://dx.doi.org/10.1016/j.autcon.2007.08.001http://dx.doi.org/10.1016/j.autcon.2007.08.001mailto:[email protected]


2/9

usage on overall project stakeholder satisfaction. Empirical

evidence that supports the link between automation usage at the

task level and project stakeholder satisfaction is lacking. Thus,

developing such support will illustrate the benefits of

automation adoption. This study attempts to fill this void of

empirical evidence by identifying project satisfaction-leverag-

ing tasks and common characteristics associated with thesecritical tasks. This paper addresses associations between task-

level automation technology usage and project success. The

links between automation utilization and project satisfaction are

explored in detail. For the purposes of this research, automation

is defined as the use of an electronic or mechanized tool by a

human being to manipulate data or produce a product[8]. The

purpose of this research is three-fold. The first objective of this

study was to identify project clusters formed on the basis of the

perceptions of stakeholder satisfaction. The second objective

was to determine project satisfaction-leveraging tasks. The third

objective was to investigate the characteristics associated with

these project satisfaction-leveraging tasks to gain deeperinsights into how automation usage may impact project success.

The analyses of automation usage and relationships with

project satisfaction are based on an industry-wide survey per-

formed between October 2004 and June 2005. A data collection

tool was developed to assess automation use levels in the

Taiwanese industry. A total of 98 project responses were collected

through personal interviews. Automation usage metrics are based

on 61 common project tasks. In order to measure the degree of

automation used on projects and its impacts on project outcomes,

the data collection tool was used on all types of projects in the

building, industrial, and infrastructure sectors. The data analyzed

in this study are project-specific, meaning the data are repre-

sentative of the levels of automation used on projects. In addition,the analyses of characterization of the project satisfaction-

leveraging tasks are based on a second survey conducted between

February 2006 and February 2007. The data collection effort

involved characterization of the project satisfaction-leveraging

tasks. This paper explains the links between automation

utilization and project success. Automation usage metrics

analyzed include those at the task level. Project success parameter

analyzed is project stakeholder satisfaction.

2. Literature review

A considerable body of research has been conducted on theadoption and use of technology in the A/E/C industry. Much of

the project/construction management literature relevant to this

research is associated with the adoption of technology, factors

influencing the implementation of technology, and the expected

benefits associated with the use of technology. Concerning

technology usage, O'Connor et al.[9]investigated the extent to

which technologies are being used in executing projects in the

construction industry. Webb et al.[10]explored the potential of

4D CAD as a tool for construction management. Abduh and

Skibniewski[11]presented an assessment model to measure the

utility of Electronic Networking Technologies (ENT) services

in construction project activities. Tse and Choy[12]carried out

an in-depth interview for studying the scope of use of IT in

Hong Kong's construction industry. Peansupap and Walker

[13] addressed the critical issue of how best to adopt and

diffuse information and communication technology (ICT) into

organizations.

While the above authors promoted the adoption of technol-

ogy, other researchers have also been active in identifying the

factors influencing the adoption of technology. Goodrum andGangwar [14] examined the relationship between changes in

equipment technology and changes in construction wages with

the help of five factors of equipment technology change: control,

energy, ergonomics, functionality and information processing.

Mitropoulos and Tatum[15]argued that uncertain competitive

advantage from using new technologies and lack of information

regarding technologies and benefits may be the reasons for

reluctance to adopt technology. There has been also much work

conducted on the benefits from technology in the construction

industry. Earlier studies supported the notion that technology

adoption is beneficial. Several researchers have investigated the

impacts of different technology on project performance. Some ofthe project performance indicators they examined include cost,

schedule, and safety success, which are of course major concerns

to project stakeholders. Fergusson [16]investigated the relation-

ships between facility integration and quality. Back and Bell [17]

identified the impacts of use of electronic data interchange (EDI)

in bulk materials management. Griffis et al. [18] studied the

impacts of using 3-D computer models on cost, schedule

duration, and rework metrics. Back et al.[19]undertook a study

to determine the impact of information management on project

schedule and cost. Tan[20]studied the impact and linkage of

information technology and competitive advantage.

Additionally, Hampson and Tatum[4]stated that technology

strategy can positively influence competitive performance.Johnson and Clayton[5]contended that information technology

can improve productivity of teams and management procedures.

Back and Moreau [6] suggested that improving internal

information exchange and integrating project-based information

across organizational boundaries may result in project cost and

schedule reductions. Thomas et al.[7] evaluated the impacts of

design/information technologies by connecting their use to

project performance in terms of cost growth, schedule growth,

and safety success. Whyte et al. [21] explored processes by

which emerging technologies can be introduced into construc-

tion organizations. Goodrum and Haas [22] investigated the

impact of different types of equipment technologies on con-struction productivity. De Lapp et al.[23]examined the impacts

of computed aided design (CAD) on design realization. Sexton

and Barrett [24] performed in-depth case study to understand

the role of technology transfer in innovation within small con-

struction firms. Lee et al.[25]examined the relative impacts of

selected practices on project cost and schedule. Above prior

studies indicated that technology is playing an important

enabling role in construction. While the diverse benefits of

technology have received substantial attention, the number of

studies dealing with technology's impact on the performance in

terms of stakeholder satisfaction is rather scarce.

A reviewof the literature suggests that the use of technologyas

a means to enhance project performance has been widely

451L.-R. Yang / Automation in Construction 17 (2008) 450458


3/9

supported. Generally, many researchers have suggested that

technology provides significant benefits to capital facility

projects. The literature review provides background for devel-

oping an understanding of the issues related to the adoption and

use of technology, factors influencing the adoption of technology,

and the benefits to be derived from technology. Issues discussed

include the use of technologies for specific project tasks,technology strategy, information technology, and the application

of integration. Project performance measurements commonly

used in the previous studies include cost, schedule, degree of

attainment, return on investment, and safety. The literature review

suggests that most performance measurements currently being

used are quantitative or hard measures of cost and schedule. Soft

measures such as leadership, employee satisfaction, and team-

work are now being measured using qualitative or subjective

measurement techniques[26]. In light of the previous research, it

is evident that additional work is justified. While research has

centered on the project performance in terms of cost and schedule

success, relatively less has approached the associations betweenautomation utilization and overall project satisfaction from the

perspectives of various types of stakeholders. Also, few articles

are known about the explanation of the links between automation

utilization and project success. Additionally, most studies in

construction seeking to provide insights into technology usage

have not developed specific measures for automation implemen-

tation and stakeholder satisfaction levels.

In summary, there has been much research conducted on the

use of technology and the benefits derived from technology

application, although there is little work with quantifiable

information on how automation affects overall project stake-

holder satisfaction. Additionally, few studies have empirically

tested the relationships between technology usage at the tasklevel and project performance in terms of stakeholder

satisfaction. Thus, it will be useful to develop quantitative

measures of automation utilization and stakeholder satisfaction.

Also, there is a need for more comprehensive empirical evi-

dence that evaluates the benefits associated with automation

and, more specifically, its impact on project performance in

terms of stakeholder satisfaction.

This research adds to the literature in two valuable ways. First,

it provides evidence of performance implications of automation

implementation at the task level. Second, it offers important

results on the identification of project satisfaction-leveraging

tasks and their common characteristics from the perspectives ofmajor stakeholders involved in projects, including the Owner,

Architect/Engineering (A/E), and General Contractor (GC)

groups. Research on automation utilization at the task level

should provide construction firms with information on whether to

adopt automationtechnologies. In addition, the assessment of task

characteristics that can leverage the benefits of automation should

also be beneficial. To address this gap in the literature, the

following research hypotheses were developed:

Levels of automation utilization for certain tasks are positively

related to projects' levels of stakeholder satisfaction

Task characteristics can explain the links between automa-

tion utilization and project stakeholder satisfaction.

3. Determining project satisfaction-leveraging tasks

3.1. Data collection tool

The first phase of research included identifying project

satisfaction-leveraging tasks. A survey instrument was used to

measure the degree of automation usage on capital facilityprojects and its associations with project outcomes. The data

collection toll was developed based on variables used in pre-

vious studies and understanding gained from interviews

conducted with executives in the construction industry. The

survey was composed of two sections: project/company

information and degree of automation use for tasks. The first

section of the survey obtains information concerning the project,

project type, and final performance of the project in terms of

stakeholder satisfaction. The second section assesses level of

automation used in executing the project. For the purpose of this

study, a project's life cycle is structured in five phases: Front

End (which includes scoping, feasibility, and preliminary designactivities), Design, Procurement, Construction Management,

and Construction Execution[9]. To provide a basis for assessing

automation usage, it is necessary to determine the common tasks

performed on projects. Based on brainstorming and the litera-

ture search [9,27], a total of 61 common project tasks were

developed. Study participants were first asked to identify a

recent project that they were familiar with for assessment. For

the subject project, the survey then asks participants to assess the

degree of automation used in executing each task for that project.

3.2. Sample selection and data collection

An industry-wide survey of automation use levels on capitalfacility projects was conducted in Taiwan between October

2004 and June 2005. A data collection tool was developed to

collect project-based data. A total of 98 project responses were

collected through personal interviews. Individuals interested in

participating in the study were identified by a search from

various industry associations. In order to obtain a truly repre-

sentative sample, not only was the geographic mix of projects

intentionally diverse, but a diverse mix of participation was

sought with respect to sector of industry. Additionally, a

specified mix of project size was targeted in order to obtain a

representative sample of the industry. More than 100 projects

were investigated and some were not included in the analysisbecause they contained insufficient information. In addition, the

projects were examined to ensure that no duplicate project

information was collected. Ultimately, 98 survey responses

were used in the analysis.

3.3. Measurement: automation usage metrics

In assessing the degree of automation used in executing each

task, respondents could choose from four levels [9,27,28]:

Level 1, where no electronic or mechanized tools are used.

Execution of task is labor intensive and involves little

mechanization. Level 2, where there are a few uncommon

electronic or mechanized tools involved but human workers

452 L.-R. Yang / Automation in Construction 17 (2008) 450458


4/9

dominate the process. Execution of task involves some

mechanization. Level 3, where there are several specialized

electronic or mechanized tools involved. Execution of task

involves more mechanization. Level 4, where fully- or nearly

fully-automated systems dominate execution of the task.

Execution of task involves mechanization linked with external

information. Levels 1, 2, 3, and 4 are associated respectivelywith the lowest, medium low, medium high, and highest levels

of automation utilization in executing tasks. The characteristics

of four levels provide quantitative measures of automation

usage for each task. In addition, Not Applicableand Don't

Knowresponses were also available for each item.

Automation index was developed to measure the use of

automation technologies for common project tasks across the

industry. For any given task, the level of automation employed

is the task score. The sum of these scores was then divided by

the total number of responses to yield the automation index for

that task. The automation index for each task is computed as

follows:

AI X

TS=NTS; 1

where AI is the Automation Index, TS is the task score, and

NTS is the number of responses with 1, 2, 3, or 4.

3.4. Measurement: project satisfaction metrics

The surveyed participants are at the project level. All major

stakeholders (including the Owner, Architect/Engineering, and

General Contractor) involved in the subject project were

interviewed to evaluate overall project satisfaction. In other

words, overall project satisfaction was measured from theperspectives of the Owner, A/E, and GC groups. The variables

of interest were assessed through the respondents' perceptual

evaluations. Items regarding project satisfaction used a five-

point scale to determine the respondents' perception of satis-

faction, where 1 represented strongly dissatisfy, 2 represented

inclined to dissatisfy, 3 represented neither satisfy nor dissatisfy,

4 represented inclined to satisfy, and 5 represented strongly

satisfy. In addition, Not Applicable and Don't Know res-

ponses were also available.

3.5. Dealing with incomplete data, reliability and validity

In order to determine if the response data associated with a

particular project were adequate to be representative, a minimum

response rate of 70% of all tasks associated with a project was

established as the criterion for acceptance. If a particular project

did not meet the 70% rate criterion, then it was not included in

the analysis. This approach helped ensure that sufficient

information was obtained about the entire project in order to

be truly representative of the actual project.

Cronbach's coefficient () was computed to test the

reliability and internal consistency of the responses. For the

items assessed in this research, the values are found to be

more than 0.90 with an average value of 0.98, which indicate a

high degree of internal consistency in the responses. Addition-

ally, two main types of validity, content and construct validity,

were tested.

The content validity of the survey used in this study was

tested through a literature review and interviews with practi-

tioners. In other words, the survey items were based on previous

studies and discussions with these construction executives. The

industry interviews encompassed a number of executives fromthe Owner, A/E, and GC groups. The refined assessment items

were included in the final survey. Finally, copies of a draft

survey were sent to several industry professions to pre-test for

the clarity of questions. Their insights were also incorporated

into the final version of the survey.

The construct validity was tested by factor analysis. Factors

were extracted using varimax rotation. As suggested by Hair

et al.[29], an item is considered to load on a given factor if the

factor loading from the rotated factor pattern is 0.40 or more for

that factor. The factor loadings for the items used in the study

are more than 0.40 with an average value of 0.71.

4. Characterizing project satisfaction-leveraging tasks

4.1. Survey instrument and process

Once project satisfaction-leveraging tasks were identified,

steps were attempted to better understand how automation

utilization affects project success. Phase 2 of the research

entailed explaining the links between automation utilization and

project satisfaction. The value of task characteristics in further

explaining the links was investigated. Task characteristics were

used to characterize the project satisfaction-leveraging tasks.

Task characteristic analysis of these critical tasks can reveal

features that leverage project performance. This approach pro-vides deeper insights into how automation usage impacts

project success.

The data collection effort involved characterization of the

project satisfaction-leveraging tasks via task characteristics. A

second data collection tool was used to assess how strongly

certain characteristics are related to a given task. Based on

brainstorming and the literature review[30], six categories of

task characteristics were developed to classify tasks by their

attributes and as a way to study differences between tasks

relative to automation usage: 1) nature of task procedures, 2)

time/space/cost factors, 3) information and data aspects, 4) task

management, 5) nature of task product, and 6) nature of humanresource.

For each subject task, the survey asks participants to assess

the extent to which individual characteristic apply to that task.

This survey offers respondents five optional responses: Strongly

Agree, Agree, Neutral, Disagree, or Don't Know. The survey of

characteristic applicability was conducted between February

2006 and February 2007.

4.2. Dealing with incomplete data, reliability and validity

In order to determine if the response data associated with a

particular task were adequate to be representative, a minimum

response rate of 70% of all characteristics associated with a task



5/9

was established as the criterion for acceptance. If responses

associated with a particular task did not meet the 70% rate

criterion, then they were not used in the analysis. This approach

helped ensure that sufficient knowledge was obtained about the

task.

In the process of determining the survey items, it is crucial to

ensure the validity of their content, which is an important

measure of a survey instrument's accuracy. A content validity

was tested through a theoretical review and interviews with

industry practitioners. Based on previous studies and discus-sions with a number of construction experts, the survey items

were accepted as possessing content validity. The draft version

of the data collection instrument was then sent to several

construction professionals to pre-test for the clarity of questions.

Their insights were incorporated into the final version of the

survey.

Based on the personal interviews conducted prior to the

survey, the targeted respondents were identified as the senior

individuals who were responsible for the project tasks. Potential

respondents to the survey were identified by these practitioners.

The respondents included presidents, vice presidents, design

managers, project managers, project engineers, and projectplanners. A specified mix of expertise, group involvement, and

industry sector participation was targeted in order to acquire a

comprehensive knowledge from different perspectives. The

data were collected from 54 industry professionals through

personal interviews. These professionals averaged 18 years of

experience. Detailed information regarding the respondents is

presented inTable 1.

4.3. Analysis of responses

For any given characteristic, the assessed degree to which a

task relates to that characteristic was established as the

Characteristic Score. In order to perform quantitative analysis,

responses were converted to Characteristic Scores as follows:

Strongly Agree = 4, Agree = 3, Neutral = 2, and Disagree = 1.

The Characteristic Applicability Index was computed as

follows:

CAI X

CS=NR; 2

where CAI is the Characteristic Applicability Index, CS is the

Characteristic Score for a task, and NR is the number of

responses with Strongly Agree, Agree, Neutral, or Disagree.

The mean Characteristic Applicability Index was then

calculated to identify the characteristics with high applicability

to the project satisfaction-leveraging tasks. A Characteristic

Applicability Index score of zero indicates not applicable.A

value of 3.00 or greater indicates highly applicable(an index

value of 3.00 is associated with Agree response). If a

characteristic has high applicability to the project satisfaction-

leveraging tasks, it indicates that this characteristic may explain

project satisfaction-leveraging. The mean Characteristic Appli-cability Index was calculated as follows:

MAI X

CAI=NST; 3

where MAI is the Mean Applicability Index, CAI is the

Characteristic Applicability Index value for a satisfaction-

leveraging task, and NST is the total number of satisfaction-

leveraging tasks assessed.

5. Results and analysis

5.1. Identification of project clusters with the same perceptions

of stakeholder satisfaction

Cluster analysis was used in an exploratory mode to develop

an objective classification of projects. This research was

exploratory; therefore, different sets of clusters consisting of

two, three, four, and five groups were examined. In order to

identify homogeneous projects clusters with the same kinds of

perceptions of stakeholder satisfaction, a K-means cluster

analysis was performed on the basis of the three dimensions

of satisfaction (Owner, A/E, and GC satisfaction). To validate

the results of the cluster analysis, a discriminant analysis was

conducted. The discriminant analysis presented in Table 2

classified 98.9% of the projects as the cluster analysis did,indicating extremely good differentiation and a correct

classification. These results suggest that the two clusters are

distinctive.

Table 1

Information regarding respondents to survey of characteristic applicability

Characteristic Classes N

Title President 06

Title Vice president 05

Title Project manager 09

Title Design manager 08Title Project engineer 19

Title Project planner 07

Expertise Need analysis 09

Expertise Budget estimate 09

Expertise Scheduling 09

Expertise Structure design 09

Expertise Electrical design 09

Expertise Project management 09

Years of experience 510 06



Group involvement Owner 27

Group involvement A/E 27

Industry sector involvement Building 22

Industry sector involvement Industrial 16Industry sector involvement Infrastructure 16

Table 2

Discriminant analysis

Number/percentage Group Predicted group

membership

Total

1 2

Number 1 70 1 71

2 0 18 18

Percentage 1 98.6 1.4 100.0

2 0.0 100.0 100.0



6/9

The cluster analysis has identified two clusters, with the

cluster mean values of discriminating variables given inTable 3.

In addition, independent-samples t tests were undertaken to

assess the internal validity of the cluster results. The results of

thettests show that statistically significant differences do exist

between the two clusters. The independent-samples t tests

shown in Table 3 confirm that the three variables of Owner

satisfaction, A/E satisfaction, and GC satisfaction do signifi-cantly differentiate across the two clusters. Combining these

results with those of the discriminant analyses, the analysis

ended with a two-cluster solution. The study reveals two project

satisfaction segments, including high project satisfaction cluster

(Cluster 1) and low project satisfaction cluster (Cluster 2). The

levels of project satisfaction by stakeholder type are shown in

Table 3. The analyses indicate that Cluster 1 has, on average,

higher levels of stakeholder satisfaction than Cluster 2 for all

satisfaction metrics analyzed. In most cases, there are only

slight differences in perception of satisfaction among the project

stakeholders. Finally, an independent-samples t test was

conducted to determine whether the data provide evidence for

significant differences in performance outcomes being associ-ated with differences in automation usage for each task. This

discussion is presented in the subsequent section.

5.2. Identification of project satisfaction-leveraging tasks

The tasks with a significant difference in automation usage

between projects with stakeholder satisfaction and dissatisfac-

tion are defined as project satisfaction-leveraging tasks. Table 4

presents the comparisons of task-level automation usage

between projects with stakeholder satisfaction and dissatisfac-

tion. The test results indicate that levels of automation usage for

these tasks may be positively associated with projects' levels ofstakeholder satisfaction.

Table 5shows the levels of automation usage for the project

satisfaction-leveraging tasks. Among the 6 critical tasks,

automation technology levels appear highest for design

structure systems and design electrical systems. Computer

Aided Design (CAD) software is commonly used to create two-

dimensional (2-D) drawings or three-dimensional (3-D) models

in the industry. CAD software can be used to generate precision

drawings or technical illustrations and refine the design at low

cost. Many organizations adopt CAD software in these designtasks. Advancements in design tasks may tend to precede the

other tasks because the limited scope aspect of design should

make related advancements more easily achievable. Also,

information- and data-intensive tasks appear to be more easily

automated than other task types. The lowest levels of automation

utilization are associated with track design progress. Data

management software may be a solution that automates the task.

A data management tool provides a means to monitor and track

design progress. It may also help design teams organize design

data. Track design progressmay lag in technology usage as a

result of inadequate automation devices driven by lack of R&D

investment. In addition, this task lags in automation usage

perhaps due to its frequent communication characteristic forwhich automation systems are difficult to accomplish. Prepare

milestone schedule and develop budget estimate are

associated with the greatest overall variability in level of

automation technology employed. While many scheduling and

cost estimating software systems exist, many of these are either

expensive or complex relative to user skills. Levels of auto-

mation utilization are the most uniform for design electrical

systems. Many organizations are making significant techno-

logical advances in the task.

Preproject planning is the project phase encompassing all the

tasks between project initial to detailed design [31]. Prior

studies have shown the importance of preproject planning onprojects and its influence on project performance [3235].

Table 3

Cluster means of discriminating variables

Variable Projects

with

satisfaction

Projects

with

dis-

satisfaction

t-statistic p-value

N Mean N MeanOwner satisfaction 76 3.67 19 2.21 10.035 0.000

A/E satisfaction 74 3.58 18 2.28 08.113 0.000

GC satisfaction 74 3.55 19 2.21 09.513 0.000

Table 4

t-Test for project satisfaction-leveraging tasks

Task Projects with stakeholder

satisfaction

Projects with stakeholder

dissatisfaction

Mean difference t-statistic Significance

N Mean Standard deviation N Mean Standard deviation

Conduct need analysis for a new facility 45 2.31 1.02 16 1.94 1.12 0.37 1.223 0.056

Develop budget estimate 67 2.45 0.99 18 2.11 1.08 0.34 1.258 0.053

Prepare milestone schedule 67 2.49 1.05 19 2.16 1.07 0.33 1.222 0.056

Design structure systems 58 2.53 0.92 18 2.28 0.89 0.25 1.039 0.076

Design electrical systems 54 2.50 0.86 17 2.29 0.77 0.21 0.878 0.096

Track design progress 60 2.07 0.88 18 1.89 0.90 0.18 0.748 0.098

Table 5

Levels of automation usage for project satisfaction-leveraging tasks

Task N Mean Standard deviation

Conduct need analysis for a new facility 62 2.21 1.01

Develop budget estimate 85 2.38 1.04

Prepare milestone schedule 86 2.42 1.06

Design structure systems 77 2.48 0.91Design electrical systems 72 2.46 0.84

Track design progress 78 2.03 0.88



7/9

Previous research indicated that greater project planning efforts

may contribute to project performance in terms of cost,

schedule, and stakeholder success. The project satisfaction-leveraging tasks identified in this study are associated with

preproject planning process, during which success in the

following phases is highly dependent on the level of effort

expended. This may be a reason why employing automation

technology in these leveragingtasks may be associated with

project stakeholder satisfaction.

5.3. Explaining the links between automation utilization and

project stakeholder satisfaction

The preliminary results from this research indicate that a total

of 6 tasks are associated with project satisfaction-leveraging

tasks. These project satisfaction-leveraging tasks are associated

with the FrontEnd and Design phases. In order to identify

characteristics associated with the project satisfaction-leverag-

ing tasks, these critical tasks were analyzed using task charac-

teristics. The data collection effort involved characterization of

these identified tasks. Task characteristics that may explain

project satisfaction-leveraging were identified to further explainthe links between automation utilization and project satisfac-

tion. Characteristic data associated with these tasks were used to

help identify common characteristic trends for project satisfac-

tion-leveraging tasks.

The critical tasks identified include: 1) conduct need analysis

for a new facility, 2) develop budget estimate, 3) prepare

milestone schedule, 4) design structure systems, 5) design

electrical systems, and 6) track design progress. The first three

are associated with the FrontEnd phase and the last three

pertain to the Design phase. Task characteristic analysis of these

critical tasks reveals features common to a specific task group.

Comparing to the Design tasks, the critical tasks associated withthe FrontEnd phase involves uncertainty or probabilistic

information. On the other hand, the critical design tasks require

frequent communication between individuals and, compared to

the FrontEnd tasks, involve more different types of organiza-

tions. Additionally, data accuracy is crucial to successful task

performance for these critical Design tasks.

The complete information of all task characteristics is

provided inTables 6 and 7.Table 6presents high applicability

characteristics for the project satisfaction-leveraging tasks.

Table 7 presents low applicability characteristics for these

tasks. These tables show the overall image of how the selected

characteristics perform. Six characteristics that may explain

Table 6

High applicability characteristics for project satisfaction-leveraging tasks

Category Task characteristics Mean

applicability

index value

Information and data Task involves uncertainty or

probabilistic information

3.60

Information and data Historical data from previous

projects are required for execution

3.11

Information and data Data accuracy is crucial to

successful task performance

3.56

Management Many different types of

organizations are involved

3.17

Management Responsible individual must

communicate frequently with others

3.58

Work procedure Task involves iterations and revisions 3.30

Table 7

Low applicability characteristics for project satisfaction-leveraging tasks

Category Task characteristics Mean applicability index value

Human resource Many individuals are involved to perform task 2.42

Human resource Task involves many individuals with different skills and specialties

Human resource User s, worker's or operator's experience is critical to performance

WF product Performance of many subsequent tasks relies heavily on this task

WF product Task product is physically large and bulky

WF product Errors are difficult to fix or require a large amount of resources to f ix

Time/space/cost Task is a critical path activity in most cases

Time/space/cost Task requires spatial coordination 2.69

Time/space/cost Task involves relatively high uncertainty in cost, schedule, quality, or safety

Time/space/cost Task management operates in close proximity to workers 2.75Time/space/cost Task involves environmental hazard

Time/space/cost Task is costly to execute

Information and data Task relies on industry technical standards 2.52

Information and data Task data are in many different formats 2.44

Information and data Security of related data is very important

Information and data Task involves significant amount of data updating

Management A specialty organization is involved in most cases 2.72

Management Primary performance driver of the task is quality, safety, cost, or schedule 1.75

Management Task involves high probability of change 2.69

Work procedure Task is error prone 2.49

Work procedure Task procedures are driven by regulations

Work procedure Task involves repetit ive activity

Work procedure Some task resources are often idle

Work procedure Task procedures are very complex

Work procedure Task relies on or requires physical output products of many previous task



8/9

leveraging were identified in the analysis. These characteristics

show a strong association with the project satisfaction-

leveraging tasks. Most of the characteristics that may explain

leveraging fall in the following two categories: 1) information

and data and 2) management. This indicates that information/

data-intensive and management-related characteristics may be

associated with project stakeholder satisfaction.Characteristics that may explain project satisfaction-leverag-

ing were identified in order to explore project stakeholder

satisfaction determinants. The analyses suggest that tasks in-

volving iterations, revisions, and many different types of orga-

nizations may deserve the execution with high automation

approaches. Tasks that require frequent communication between

individuals may be associated with project stakeholder satisfac-

tion. In addition, degrees of automation used in executing the

tasks that involve uncertainty or probabilistic information may

be positively related to the stakeholder satisfaction of a project.

The priority for automation implementation may also pertain to

the tasks for which data accuracy is critical to performance andhistorical data from previous projects are required for execution.

One of the low applicability characteristics (Primary

performance driver of the task is cost, schedule, quality, or

safety) was further analyzed to explain the links between

automation utilization and project satisfaction. Regarding

primary performance driver of the satisfaction-leveraging

tasks, the applicability index values for cost, schedule, quality,

and safety are 1.50, 2.25, 2.25, and 1.00 respectively (with a

mean of 1.75). The relatively high levels of applicability are

associated with schedule and quality. The analyses suggest that

primary performance driver of the leveraging tasks may be

more closely associated with schedule and quality than are cost

and safety.

6. Conclusions and recommendations

The lack of information regarding automation benefits along

with uncertain competitive advantage from new technology

have resulted in industry reluctance to implement new

automation technologies. Thus, a study of the relationship

between automation utilization and project stakeholder satis-

faction is necessary. The purpose of this study was to identify

project satisfaction-leveraging tasks and common characteris-

tics associated with these critical tasks. Metrics were developed

to determine automation usage at the task level and perceivedstakeholder satisfaction. The projects are examined by cluster-

ing them on the basis of differences in perceptions of the

proposed satisfaction dimensions. In other words, cluster

analysis was used as a means to group similar properties on

the basis of stakeholder satisfaction. Independent-samples t

tests were undertaken to assess the internal validity of the

cluster results. Combining these results with those of the

discriminant analyses, a two-cluster solution was identified. The

study reveals two project satisfaction segments, including high

project satisfaction cluster and low project satisfaction cluster.

Furthermore, hypothesis testing was performed to identify

relationships between task-level automation utilization and

project satisfaction. For each of the tasks, levels of automation

usage were analyzed to identify project satisfaction-leveraging

tasks. The tasks with a significant difference in automation

usage between projects with stakeholder satisfaction and

dissatisfaction are associated with project satisfaction-leverag-

ing tasks. In other words, the project satisfaction-leveraging

tasks have significant differences in automation usage for

projects with stakeholder satisfaction as opposed to projectswith stakeholder dissatisfaction. These project satisfaction-

leveraging tasks are associated with the FrontEnd and Design

phases. The critical tasks identified in this study include: 1)

conduct need analysis for a new facility, 2) develop budget

estimate, 3) prepare milestone schedule, 4) design structure

systems, 5) design electrical systems, and 6) track design

progress.

This paper also explores the links between project

satisfaction and automation utilization in detail. The technique

used for analyzing the associations is analysis of task

characteristics. The project satisfaction-leveraging tasks were

further analyzed using characteristic analysis to explain thelinks between automation utilization and project success. In

other words, task characteristics were used to better understand

project satisfaction-leveraging tasks through analysis of their

attributes. In summary, task characteristics analyses of project

satisfaction-leveraging tasks are employed as a way to gain

greater understanding of the connection between automation

usage and project satisfaction. The characteristic analysis

reveals attributes common to the project satisfaction-leveraging

tasks. These analyses suggest that data/information-intensive

and management-related task characteristics may be associated

with project stakeholder satisfaction. Degrees of automation

used in executing the tasks that require frequent personnel

communications may be positively related to the stakeholdersatisfaction of a project. Consideration should be also given to

employing higher levels of automation usage for the tasks that

involve many different types of organizations. Furthermore, the

priority for automation implementation may also pertain to the

tasks for which data accuracy is critical to performance and

historical data from previous projects are required for execution.

In addition, tasks that involve iterations, revisions, uncertainty,

or probabilistic information may deserve the high automation

approaches.

The research provides empirical evidence that supports the

expectation of gaining significant benefits with higher levels of

automation implementation. This paper reports on the findingsof empirical research on the adoption of automation technology

at the task level, identifies the benefit of adopting automation

technology, and provides recommendations for the implemen-

tation of automation technology on projects. The results of this

study indicate that automation is critical to assist in the exe-

cution of project tasks and may contribute significantly to

project performance in terms of stakeholder satisfaction.

Findings from this study provide direction for the decision

making of automation investment and are helpful to managers

in deciding whether to apply automation to tasks with certain

characteristics on capital facility projects.

One limitation of the research is that qualitative factors are

excluded from the analysis. The qualitative issues may be



9/9

significant in helping explain the associations between

automation utilization and project performance. In spite of the

limitation of acquisition of qualitative information, character-

istic applicability analysis is a logical approach for exploring the

links. However, it would be worthwhile to examine the quali-

tative factors in future studies.

Acknowledgments

The author would like to thank the anonymous referees for

their extremely helpful comments on this paper. The work

described in this article was supported by grant from the

National Science Council of Taiwan (93-2211-E-032-024 and

94-2211-E-032-021).

References

[1] CERF, Commercializing infrastructure technologies, CERF Report #97-

5028, Civil Engineering Research Foundation, Reston, VA, 1997.

[2] P. Mitropulos, C.B. Tatum, Process and criteria for technology adoption

decisions, Proceedings of Construction Congress, San Diego, CA, 1995,

pp. 1724.

[3] P. Simmons, Measurement and the evaluation of I.T. investments,

Proceedings of IEEE 2nd International Software Metrics Symposium,

London, England, 1994, pp. 7483.

[4] K. Hampson, C.B. Tatum, Technology strategy and competitive

performance in bridge construction, Journal of Construction Engineering

and Management 123 (2) (1997) 153161.

[5] R.E. Johnson, M.J. Clayton, The impact of information technology in

design and construction: the owner's perspective, Automation in

Construction 8 (1) (1998) 314.

[6] W.E. Back, K.A. Moreau, Cost and schedule impacts of information

management on EPC process, Journal of Management in Engineering 16

(2) (2000) 5970.

[7] S.R. Thomas, C.L. Macken, S.H. Lee, Impacts of Design/Information

Technology on Building and Industrial Projects, BMM2001-10, Construc-

tion Industry Institute (CII), Austin, TX, 2001.

[8] J.T. O'Connor, L. Yang, Project performance vs. use of technologies at the

project- and phase-levels, Journal of Construction Engineering and

Management 130 (3) (2004) 322329.

[9] J.T. O'Connor, M.E. Kumashiro, K.A. Welch, S.P. Hadeed, K.E. Braden,

M.J. Deogaonkar, Project- and Phase-Level Technology use Metrics for

Capital Facility Projects, Report, vol. 16, Center for Construction Industry

Studies, Austin, TX, 2000.

[10] R.M. Webb, J. Smallwood, T.C. Haupt, The potential of 4D CAD as a tool

for construction management, Journal of Construction Research 5 (1)

(2004) 4360.

[11] M. Abduh, M.J. Skibniewski, Electronic networking technologies in

construction, Journal of Construction Research 5 (1) (2004) 1742.

[12] R.Y. Tse, L. Choy, Is IT training in construction industry useful, Journal of

Construction Research 6 (1) (2005) 113.

[13] V. Peansupap, D. Walker, Factors affecting ICT diffusion: a case study of

three large Australian construction contractors, Engineering, Construction

and Architectural Management 12 (1) (2005) 2137.

[14] P.M. Goodrum, M. Gangwar, The relationship between changes in

equipment technology and wages in the US construction industry,

Construction Management and Economics 22 (3) (2004) 291301.

[15] P. Mitropoulos, C. Tatum, Factors driving adoption of new information

technologies, Journal of Construction Engineering and Management 126

(5) (2000) 340348.

[16] K.J. Fergusson, Impact of integration on industrial facility quality, PhD

Thesis, Stanford University, 1993.

[17] W.E. Back, L.C. Bell, Quantifying Benefits of Electronic Technology

Applied to Bulk Materials Management, The Construction Industry Action

Group, Houston, TX, 1994.[18] F.H. Griffis, D.B. Hogan, W. Li, An analysis of the impacts of using three

dimensional computer models in the management of construction,

Research Report 106-11, Construction Industry Institute (CII), Austin,

TX, 1995.

[19] W.E. Back, K.A. Moreau, J. Toon, Determining the impact of information

management on project schedule and cost, Research Report 125-11,

Construction Industry Institute (CII), Austin, TX, 1996.

[20] R.R. Tan, Information technology and perceived competitive advantage:

an empirical study of engineering consulting firms in Taiwan, Construction

Management and Economics 14 (3) (1996) 227240.

[21] J. Whyte, D. Bouchlaghem, T. Thorpe, IT implementation in the

construction organization, Engineering, Construction and Architectural

Management 9 (56) (2002) 371377.

[22] P.M. Goodrum, C.T. Haas, Partial factor productivity and equipment

technology change at activity level in U.S. construction industry, Journal ofConstruction Engineering and Management 128 (6) (2002) 463472.

[23] J.A. De Lapp, D.N. Ford, J.A. Bryant, J. Horlen, Impacts of CAD on

design realization, Engineering, Construction and Architectural Manage-

ment 11 (4) (2004) 284291.

[24] M. Sexton, P. Barrett, The role of technology transfer in innovation within

small construction firms, Engineering, Construction and Architectural

Management 11 (5) (2004) 342348.

[25] S. Lee, S.R. Thomas, R.L. Tucker, The relative impacts of selected

practices on project cost and schedule, Construction Management and

Economics 23 (5) (2005) 545553.

[26] J.D. Stevens, C. Glagola, W.B. Ledbetter, Quality-measurement matrix,

Journal of Management in Engineering 10 (6) (1994) 3035.

[27] K.A. Welch, Development of a tool for assessing the degree of automation

and integration on capital projects, MS Thesis, The University of Texas at

Austin, 1998.[28] M.E. Kumashiro, Measuring automation and integration in the construc-

tion industry, MS Thesis, The University of Texas at Austin, 1999.

[29] J.F. Hair, R.E. Anderson, R.L. Tatham, W.C. Black, Data Analysis with

Readings, 4th ed.Prentice-Hall, Englewood Cliffs, NJ, 1995.

[30] S. Won, A model for work function-based prioritization of technologies for

capital projects, PhD Thesis, The University of Texas at Austin, 2002.

[31] C.S. Cho, G.E. Gibson, Building project scope definition using project

definition rating index, Journal of Architectural Engineering 7 (4) (2001)

115125.

[32] L. Yang, J.T. O'Connor, C. Huang, Comparison of technology utilization

and benefits in Taiwanese and U.S. industries, Journal of Management in

Engineering 23 (3) (2007) 147155.

[33] L. Yang, J.T. O'Connor, J. Chen, Assessment of automation and

integration technology's impact on project stakeholder success, Automa-

tion in Construction 16 (6) (2007) 725733.[34] A.F. Griffith, G.E. Gibson, Alignment during preproject planning, Journal

of Management in Engineering 17 (2) (2001) 6976.

[35] A.F. Griffith, G.E. Gibson, M.R. Hamilton, A.L. Tortora, C.T. Wilson,

Project success index for capital facility construction projects, Journal of

Performance of Constructed Facilities 13 (1) (1999) 3945.


Documents

Exploring the links between task-level automation usage.pdfAbstract Hybrid assembly systems consist of an automatic assembly section that feeds a manual finishing line. They are competitive