Development of Effective Communication Framework

Development of Effective Communication Framework Using Confirmatory Factor Analysis Technique Thahomina Jahan Nipa, S.M.ASCE1, Sharareh Kermanshachi, Ph.D., M.ASCE2 and Shirin Kamalirad, S.M.ASCE3 1

Ph.D. Student, Department of Civil Engineering, University of Texas at Arlington, 425 Nedderman Hall, 416 Yates Street, Arlington, TX 76019. E-mail: [email protected] 2 (Corresponding author) Assistant Professor, Department of Civil Engineering, University of Texas at Arlington, 438 Nedderman Hall, 416 Yates Street, Box 19308, Arlington, TX 76019. Email: [email protected] 3 Graduate Student, Department of Civil Engineering, University of Texas at Arlington, 425 Nedderman Hall, 416 Yates Street, Arlington. TX 76019. E-mail: [email protected] ABSTRACT Primary stakeholders (owner, consultant, and contractor) are commonly subject to challenges associated with internal miscommunication. Inaccurate transmission of data may cause major project delays and cost overruns. As it was found that few researchers have focused on prediction and analysis of designers’ effective communication indicators (DECI) in construction projects, this study aims at determination of DECIs in construction projects. For this purpose, a survey containing 52 questions corresponding to potential effective communication indicators was developed and distributed among experienced designers. Based on survey responses and implemented statistical tools, DECIs were identified. Confirmatory factor Analysis technique was then utilized to determine major components of DECI. Results revealed that principal components of DECI are design and technology, scope clarity, technical and financial support, facility, experience issues, and decision-making issues. This study assists practitioners make proactive plan to utilize project resources properly in order to manage internal miscommunication. INTRODUCTION Construction projects are like complex networks where human, information, resources and tasks act as nodes of these networks and through communication these nodes stay linked to each other (Kamaliad and Kermanshachi 2018). With the increasing rate of urbanization and population growth, construction industries are continuously expanding beyond the national boundary and always in search for more revenues (Jevernick-Will and Scott 2009). Two of the major factors that affect the revenue of construction projects are time and cost performance. Unfortunately, miscommunication within primary stakeholders (owners, engineers, and contractors) and secondary stakeholders (subcontractors, vendors, and suppliers) are most likely to affect time and cost performance negatively (Kamalirad and Kermanshachi 2018). The design phase is one of the most important phases of the construction projects (Habibi et al., 2018) and the majority of the time, the success of this phase depends on winning a very competitive bid. This nature of Design phase oftentimes generates mistrusts and the conflicting relationship among stakeholders (Hartmann and Caerteling 2010). Moreover, the current construction industry is dealing with megaprojects involving multiple designers, thus increases the possibility of friction within them. Effective communication places a vital role to clear out this friction, on the contrary, miscommunication fuels it which cost time and resources out of the project (Fulford and Standing 2014; Safapour et al., 2018). Yet, literature provides very limited materials that describe the factors affecting internal communication among designers. 1

Therefore, the aim of this study is to identify DECI after analyzing the impact of designers’ internal communication on the performance of complex projects. In addition to identifying DCEIs, this study also aims to reduce the dimension of indicators into a limited number of components to find out the similarity among indicators and their relative importance. The outcome of this paper will help project managers in identifying the stakeholder's needs in communication and formulating proper engagement strategy to ensure effective communication. Thus, this study solves the often-faced challenges of project managers in complex construction projects (Mok et al. 2015). LITERATURE REVIEW The construction industry is taking full advantage of the open market by expanding its reach to beyond the national border with the help of advanced transportation and communication system (Ngowi et al. 2005). However, a slight miscommunication resulting in a combative relationship can ruin this progress for the construction industry as it is very competitive in nature (Chan et al. 2004). Likewise, throughout the literature, many researchers identified ineffective communication as one of the main causes of delay in construction projects (Larsen et al. 2015; Liu et al. 2007; Assaf and A-Hejji 2006). Communication in general sense can be defined as an exchange of thoughts and information from one source to another (Perumal and Abu-Bakar 2011). This simple definition is not adequate for construction industry where multiple stakeholders come together for a very short period of time with a specific aim in mind and required to collaborate together to fulfill the goal of the project (Murray et al. 2006). In this sector, communication can be seen as a generating, collecting, disseminating, storing and transferring process of information among or within the stakeholder entities (Perumal and Abu-Bakar 2011). Many researchers over the decades tried to find out the relationship between effective communication and project performance (Kamalirad et al., 2017). For example, Sambasivan and Soon (2007), after conducting a questionnaire survey among 150 owner, contractors, and consultants, confirmed that without proper communication, involved parties misunderstand each other which eventually delays the execution of the project. Ejohwomu et al. (2017) found out that construction project teams loose trust due to ineffective communication which hampers project performance. Similarly, Kermanshachi (2016) found out that, communication quality and project performance is positively correlated. In addition, the present construction market uses partnering nationally and internationally to handle complex projects (Safapour et al., 2017). This partnership will result in output with minimum cost and time overruns only when effective and open communication environment will exist among teams (Chan et al. 2004). In fact, the success of construction projects significantly depends on trustworthy and open communication environment (Becker et al. 2011). Besides, establishing the reasons behind miscommunication among stakeholders is also important (Safapour et al., 2019). Ejohwomu et al (2017) found out that ambiguous project goal, faulty reporting scheme, and poor leadership act as the main hindrance for effective communication based on the opinion of 100 contractors and consultants. Moreover, Odeh and Battaineh (2002) found that a lack of proper communication affects the performance of designer entities more than contractor entities. However, most of the above-mentioned studies focused on the communication within multiple stakeholders and develops corollaries based on the relationship among them. Likewise, current literature contains a lot of studies where the communication

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indicators were established for multiple stakeholders. As a result, existing literature does not provide enough materials that ensure required attention for a particular stakeholder. RESEARCH METHODOLOGY The methodology adopted in this paper can be divided into four steps shown in Figure 1. In the first step, a thorough search through literature is performed to find out the existing works related to impacts of communication of designers on project performance and ultimate success of the project. Based on reviewed scholarly articles, a list of potential DECIs and the related attributes were identified and categorized.

Figure 1. Research Methodology The second step focused on the data collection process. Data collection step started with the development of a comprehensive survey on the DECIs. The questionnaire used in the survey was finalized after conducting pilot testing. The finalized survey was distributed among designers who are actively working on design problems of construction projects. After multiple follow-ups, the completed surveys were collected for further analysis. In the third step, two sample test and chi-squared test were used to identify effective communication indicators among designers in construction projects. Based on the test results, indicators were finalized. In the next step, factor analysis was done to reduce the number of identified DECIS for future modeling purposes. DATA COLLECTION To find out the relevance and importance of DECIs in the present condition of the construction industry, 52 questions were generated, and a pilot questionnaire survey was developed. Based on the responses of four pilot tests, the pilot survey questions were modified in a manner so that the questions were clearly understandable by the intended participants. Based on the type of response, the survey questions were categorized into three categories, continuous, Likert scale, and binary formats. Few sample questions are shown in Table 1 below. After appropriate modifications, the survey was sent to prominent construction practitioners mainly involved in design projects with at least 10 years of experience. After several follow-up emails, 30 completed response were collected grouped in two categories: complex 3

projects with effective communication and complex projects with ineffective communication. The continuous, Likert scale and binary questions went through two-sample t-test, analysis of variance and Chi-squared test respectively. These statistical tests were done considering both 90% and 95% level of confidence to determine the significant DECIs based on the relation between quality of communication and potential indicators. Table 1. Sample questions for the survey Type

Question (options for response)

Continuous

How many different countries worked on the detailed engineering/design phase of the project? (Number: __________)

Likert scale

What was the difficulty in obtaining design approvals? (Scale 1 to 7, 1 being not at all difficult, 4 being moderately difficult and 7 being extremely difficult)

Binary

Did the project experience any delays or difficulties in securing project funding? (a. yes, b. no)

DATA ANALYSIS After analyzing survey responses, 35 indicators were listed, and two sample t-test was performed. Table 2 shows 17 significant indicators which are categorized into seven categories. These 17 indicators significantly define whether the designers will have effective communication in a complex construction project. Table 2. Significant designer’s internal communication quality indicators Designer’s Communication Indicators

Category

DECI #

Bureaucracy Coordination

DECI 7

Impact Of Required Approvals-Internal Stakeholders

**0.021

DECI 16

Difficulty Level In Obtaining Permits

**0.036

DECI 10

Project Management Team Peak Size – Procurement Phase

*0.073

DECI 15

Project Management Team Experience – Construction Phase

**0.009

DECI 17

Number Of Designer/Engineer Organizations

**0.036

Location

DECI 8

Number Of Countries Involved In Construction Phase

*0.062

Scope Definition

DECI 4

Clarity Of Projects Scope During Designer/Contractors Selection

**0.040

DECI 5

Clarity Of Owners Project Goals And Objective

**0.008

DECI 9

Bulk Materials Quality Issues

**0.015

DECI 11

Degree Of Additional Construction Specifications

*0.051

DECI 13

Delay In Delivery Of Permanent Facility Equipment

*0.053

DECI 6

Clarity Of Funding Process During Front End Planning

*0.093

DECI 12

Project Funding Delays

*0.072

DECI 1

Company’s Familiarity With Technologies – Construction Phase

**0.030

DECI 2

Company’s Familiarity With Technologies – Engineering Phase

**0.014

DECI 3

Company’s Familiarity With Technology – Operation Phase

*0.061

Interface

Material Resources Economic Issues

Technology

DECI 14 Number Of New Systems Tied Into Existing Systems **denotes significance with 95% level of confidence *denotes significance with 90% level of confidence

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Sig.

**0.029

Dimension Reduction This study used statistical package for social science (SPSS v. 10) to run factor analysis. Before proceeding to factor analysis, it must be determined whether the data is adequate for factor analysis. To establish that point, Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy test and Bartlett’s test of Sphericity were conducted. The KMO value is found to be 0.508 which is greater than the least acceptable limit of 0.5 (Fadun and Saka 2018). The Bartlett’s test of Sphericity was found to be significant at 0.001 which is less than the maximum acceptable limit of 0.005 (Priyanka 2017). Both tests indicated that our variables are significantly correlated, hence appropriate for factor analysis. The result of KMO and Bartlett’s test of Sphericity are shown in Table 3. Table 3. KMO and Bartlett’s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy Bartlett's Test of Sphericity

Approx. Chi-Square df Sig.

0.508 195.326 136 0.001

To find out the similarity of the indicators, factor analysis was utilized. After using this technique, the indicators were grouped into six components with minimum significant loading value of 0.4 which can be taken as standard (Timothy 2014). Each component is a collection of interrelated indicators and these six components explain 70.62% of the variables. Table 4 shows the rotated component matrix from SPSS factor analysis. The first component which constitutes the largest part of total variance is Design and Technology. The percentage of total variance for this component is 17.05% and includes five indicators (DCEI 1, 2, 3, 6 and 12). Being the first component of factor analysis, Design and Technology component holds the maximum importance in designer’s effective communication. In other words, using commonly used technologies makes the designers work more familiar with each other leading to effective communication with each other. The second component is Scope Clarity with a total variance of 16.97%. This component includes DCEI 4, 5, 6, 7, and 9. Having clear knowledge about the owner’s requirement from the beginning of the project leads to a well-defined scope (Kermanshachi et al., 2017) and results in smooth communication for the designers. Besides, the process by which the project is getting funded, helps the designer to have an idea regarding the scope and timeline of the project, hence clear knowledge regarding this process helps in communication. In addition, a clear idea of a number of national and international stakeholders’ involvement and quality of material almost always makes the communication effective among designers. The third component is Technical and Financial Support with a total variance of 12.96%. this component includes DCEI 10, 11 and 12. The size of team the designers must work with is another factor which affects the effective communication among designers. Also, the degree of having additional construction specification and availability of funding to adopt these additions also affect designer’s communication. The fourth component is Facility with a total variance of 8.75%. This includes DCEI 13 and 14. Designers start working at the beginning of the project and keep working throughout the project lifecycle. Not having permanent facility equipment delivered at the right time will delay their work which will affect the whole project life, this phenomenon will affect the communication of designers. Also, having a combination of new systems with old systems will make it difficult 5

for them to communicate with each other, especially if the experience of working with different systems/facilities vary for different designer entities. The fifth component is Experience Issues with a total variance of 7.77%. this includes DCEI 16 and 17. Designers have to obtain a permit with their design before starting construction based on their design. Having a complex system to obtain the permits often time affect the internal communication of designers as it is seen from factor analysis. However, experienced team can manage this complexity more efficiently, thus having proper experience is also important for effective communication among designers. Table 4. Rotated component matrixa

DECI 1

Company’s Familiarity with Technologies -Construction phase

Component 1 2 3 4 5 6 .863 .123 -.076 .007 .116 -.085

DECI 2

Company’s Familiarity with Technologies -Engineering phase

.829 .049 -.039 -.075 .176 .100

DECI 3

Company’s Familiarity with Technologies -Operation phase

.823 -.046 .121 .149 -.036 .195

DECI 4

Clarity of Projects Scope During Designer/Contractor Selection

.069 .860 .006 .095 .107 .009

DECI 5

Clarity of Owners Project Goals and Objectives

DECI 6

Clarity of Funding Process during Front End Planning

.488 .706 .182 -.006 .031 -.034

DECI 7

Impact of Required Approvals-Internal Stakeholders

.040 .639 .047 -.200 -.371 .001

DECI 8

Number of Countries Involved in Construction Phase

-.277 .471 .299 .317 .127 .392

DECI 9

Bulk Materials Quality Issues

-.016 .407 .332 .250 -.257 .309

DECI #

DECI Description

DECI 10 Project Management Team Peak Size-Procurement Phase

-.047 .832 .013 .168 -.064 -.107

-.223 -.094 .826 .065 .049 -.156

DECI 11 Degree of Additional Construction Specifications

.027 .223 .696 -.014 .096 .206

DECI 12 Project Funding Delays

.470 .074 .693 .059 -.117 -.016

DECI 13 Delay in Delivery of Permanent Facility Equipment

-.009 .142 -.036 .886 .076 -.001

DECI 14 Number of New Systems Tied into Existing Systems

.347 .017 .373 .569 -.311 -.042

DECI 15 Project Management Team Experience -Construction Phase

.145 -.120 -.012 .050 .779 -.098

DECI 16 Difficulty Level in Obtaining Permits

.158 .158 .397 -.313 .551 .295

DECI 17 Number of Designer/Engineer Organizations

.160 -.119 -.021 -.045 -.038 .853

Extraction Method: Principal Component Analysis Rotation Method: Varimax with Kaiser Normalization a. Rotation converged in 7 iterations

The sixth component is Decision-Making Issues with a total variance of 7.12%. This includes DCEI 17 which is a number of designer/engineer organization. It is almost always difficult to maintain effective communication especially for making an important decision when the number of involved parties is large. Similarly, complex projects with many designer firms’ involvement often time suffer from ineffective communication regarding decision making. CONCLUSION Complex projects require multiple designers working together and whenever there is involvement of more than one entity with similar responsibility and authority, there will be some friction. Hence, it was the aim of this study was to develop designers’ internal communication indicators 6

to help smooth out this friction. This study pointed out six set of indicators which depicts the possible causes of designers’ ineffective communication. This six sets or components were named as design and technology, scope clarity, technical and financial support, facility, experience issues, and decision-making issues. This study also prioritized these components by their percentage of total variance. Therefore, this study not only helps project managers to identify the causes beforehand and take proper measures to reduce the friction based on indicators but also direct project managers to focus on communication factors based on their importance level. However, communication is a changing matter which depends on time and culture. So before applying the result of this study one should consider the effect of time and place appropriately if needed. REFERENCES Assaf, S. A., and Al-Hejji, S. (2006). “Causes of delay in large construction projects.” International journal of project management, 24(4), 349-357. Becker, T. C., Jaselskis, E. J., and McDermott, C. P. (2011, April). “Implications of construction industry trends on the educational requirements for future construction professionals.” In Proceedings of the Associated Schools of Construction 2011 International Conference, Omaha, NE (pp. 1-12). Brown, T. A. (2014). Confirmatory factor analysis for applied research. Guilford Publications. Chan, A. P., Chan, D. W., Chiang, Y. H., Tang, B. S., Chan, E. H., and Ho, K. S. (2004). “Exploring critical success factors for partnering in construction projects.” Journal of construction engineering and management, 130(2), 188-198. Chan, A. P., Scott, D., and Chan, A. P. (2004). “Factors affecting the success of a construction project”. Journal of construction engineering and management, 130(1), 153-155. Ejohwomu, O. A., Oshodi, O. S., and Lam, K. C. (2017). “Nigeria’s construction industry: barriers to effective communication.” Engineering, Construction and Architectural Management, 24(4), 652-667. Fadun, O. S., and Saka, S. T. (2018). “Risk management in the construction industry: Analysis of critical success factors (CSFS) of construction projects in Nigeria.” International Journal of Development and Management Review, 13(1). Fulford, R., and Standing, C. (2014). “Construction industry productivity and the potential for collaborative practice.” International Journal of Project Management, 32(2), 315-326. Habibi, M., Kermanshachi, S., and Safapour, E. (2018), “Engineering, Procurement and Construction Cost and Schedule Performance Leading Indicators: State-of-the-Art Review,” Proceedings of Construction Research Congress, ASCE, New Orleans, Louisiana, April 2-4, 2018. Hartmann, A., and Caerteling, J. (2010). “Subcontractor procurement in construction: the interplay of price and trust.” Supply chain management: an international journal, 15(5), 354-362. Javernick-Will, A. N., and Scott, W. R. (2009). “Who needs to know what? Institutional knowledge and international projects.” Submitted to: Journal of Construction Engineering and Management (under review). Kamalirad, S., and Kermanshachi, S. (2018). “Development of Project Communication Network: A New Approach to Information Flow Modeling,” Proceedings of Construction Research Congress, ASCE, New Orleans, Louisiana, April 2-4, 2018. Kamalirad, S., and Kermanshachi, S. (2018). “Development of Project Life Cycle Communication Ladder Framework Using Factor Analysis Method.” Proceedings of Construction Research Congress, ASCE, New Orleans, Louisiana, April 2-4, 2018. 7

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