120-748-1-PB[1].pdf

7/29/2019 120-748-1-PB[1].pdf

1/14

International Journal of Architecture, Engineering and Construction

Vol 1, No 4, December 2012, 199-212

A Framework for Performance Assessment of

Organizations in the Construction Industry

Tarek Zayed1,, Emad Elwakil2, Mohammad Ammar3

1Building, Civil, and Environmental Engineering Department, Concordia University,

Montreal, Quebec H3G 1M8, Canada

2Department of Civil Engineering and Construction Management, California State University,

Northridge, CA 90802, United States

3Structural Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt

Abstract: Competition and customer needs forced construction companies (organizations) to measure theirperformance beyond the financial aspects. Success, the main driver of any organization, depends mainly ona variety of factors that impact organizations performance. Predicting the performance of a constructionorganization helps in identifying the weak points and in searching solutions to improve its performance andincrease profit. Due to the diversity and complexity of construction organizations, it is intricate to adopt ascientific strategy that measures their success. Previous research works showed a lack of attention towardsmodeling organizations non-financial performance while focusing on measures of project success. Therefore,the objective of the present research is to identify and study the success factors and to develop performanceprediction model(s) for construction organizations. The potential success factors are collected from literature

and practitioners through a questionnaire that is prepared and sent to evaluate the effect of these potentialsuccess factors on organizational performance. The collected data are analyzed using artificial neural network(ANN) to determine the most significant (critical) success factors. Two performance prediction models aredeveloped using ANN and regression analysis, which show robust results when verified and tested. The analysisshows that the developed models are sensitive to the identified critical success factors.

Keywords: Construction Industry, organization performance, critical success factors, artificial neural networks,regression analysis

DOI: 10.7492/IJAEC.2012.022

1 INTRODUCTION

Globalized competition and customer needs forcedconstruction companies to assess their performancebeyond the financial measures, i.e., profitability;turnover, etc (Isik et al. 2010). Profit and successare considered the main drivers of any organization.Achieving success depends on many factors which havedirect effect on the performance of organizations. Inthe construction industry, it is even more difficult toadopt or maintain a scientific strategy to measure suc-cess due to the diversity and complexity of construc-tion organizations (Abraham 2002). Predicting perfor-mance of construction organizations helps define weak

points in order to improve their performance and to

increase profit. Kaplan and Norton (1993) concludedthat it is critical to measure and control new strategies

after they have been put to work at the operationalphase of construction organizations. In measuring theperformance of a business on executive monitoring sys-tem, Holophan (1992) proved that critical success fac-tors (CSFs) are the best methodology to develop anexecutive monitoring system to contain corporate-wideindicators of success, which is the main goal of any or-ganization. Many research efforts have been done todetermine the success factors. However, many of thesestudies have been done at the project level rather thanat the organizational level.

The determination of success factors was approached

through various methods. The most commonly utilized

*Corresponding author. Email: [email protected]

199
mailto:[email protected]://dx.doi.org/10.7492/IJAEC.2012.022http://-/?-

7/29/2019 120-748-1-PB[1].pdf

2/14

Zayed et al./International Journal of Architecture, Engineering and Construction 1 (2012) 199-212

methods are questionnaires and interviews with tech-nical persons and professionals. Most of the construc-tion organizational success factors are qualitative innature rather than quantitative. To accomplish suc-cess of a construction organization, there is a need

to determine these success factors, which can then beused later to predict and improve organizational per-formance. Modeling the performance of constructionorganizations from financial prospective are extensivelyresearched, however, modeling the performance consid-ering non-financial aspects does not receive sufficientattention from researchers.

Therefore, the scope of the present study is to investi-gate the most significant organizational success factorswith focus on construction organizations and develop aframework for predicting organizational performance.This paper is organized as follows: a comprehensiveliterature review on success factors followed by a dis-

cussion of the previous modeling work that has beencarried-out. Extensive efforts are done to collect datafrom various construction organizations. The collecteddata are used to develop two organizational perfor-mance models using artificial neural network (ANN)and regression analysis. The developed models aretested and verified.

2 RESEARCH OBJECTIVES

The objectives of the present study are summarized asfollows:

1. Identify and study the success factors for the con-struction industry at organizational level.

2. Determine the significant factors (i.e., critical)that mostly contribute to the organization per-formance.d bad weather.

3. Develop organizational performance model(s)/framework(s) based on these critical success fac-tors.

3 BACKGROUND

Organization as a term is defined, according tothe American Heritage College Dictionary (1993), as:An organization is a structure through which in-dividuals cooperate systematically to conduct busi-ness. Aldrich (1979) described the organization as aseries of Goal directed boundary-maintaining activitysystems. The definition of success has been changedand more developed to include quality as an indicationof success. The project success differs from one personto another and the success criteria change according tothe project itself. However, success is mostly defined asthe overall achievement of project/organization goals

and expectations (Parfit and Sanvido 1993). Manufac-turing, communication, and other industries used therevenue growth rate over a period of time as a factor

to determine success. The previous definitions of suc-cess are project oriented. Moreover, success factors,quantitative or qualitative, are not easily measurable.Success factors as a concept and their applications tobusiness are not new; it dated back to 1960s by Daniel

(1961). Rockart (1982) described the principles of suc-cess factors to make a systematic way of identifying theneeded information for executives. Organizational per-formance comprises the actual output of an organiza-tion as measured against its intended/planned outputs(i.e., goals and objectives).

In addition, the success of organizations is less depen-dent on attractiveness of its industry or countrys en-vironment; however, it is more reliant on firm-specificfactors that control its competitive advantage (Hawaw-ini et al. 2004). Companies need to expand their think-ing beyond national borders when it comes to com-petition, capabilities, and customers (Wethyavivornet al. 2009). Globalization methodologies affect theconstruction industry in many ways. It can facil-itate a markets expansion from a local market toan international one. A single company can haveprojects in different countries; for example, Venezuela,Algeria, China and United Arab Emirates. As aconsequence of this expansion, construction organi-zation faces new competitors from different coun-tries (Shurchuluu 2002).

Rockart and Bullen (1981) differentiated betweenthe goals and success factors through the traditionalstrategic planning and management, where goals and

objectives are fairly well known but success factorsdefinition is much less clear. However, the organiza-tion goals are defined to be the targets establishedto achieve the organizations mission. They are veryspecific and well known what to accomplish, when tobe achieved, and by whom in a quantitative measur-able approach (Richard 2004). Rockart (1979, Rockart(1982) identified four prime sources of success factorsfor any organization working in any industry, whichare: (1) structure of the industry, (2) competitive strat-egy, industry position, and geographic location, (3) en-vironmental factors, and (4) temporal factors. Ahmad

and Dye (1994) addressed the common essential at-tributes of business organizations throughout an ex-tensive literature. These attributes should be takeninto consideration while analyzing organization perfor-mance. The organizational performance criteria andtheir factors are summarized by Caballero and Dye(1999). They include several main criteria, such asbusiness experience, personnel, and financial. The fac-tors that impact each criterion are also listed, such asthe number of years in business and the type of busi-ness. There are also several factors that impact person-nel criterion, such as the number of full time employees,average length of time employed, the ratio of supervi-

sors to workers, the level of training for supervisors,etc. In addition, annual revenues, liquid assets, dollarvalue of lines of credit, and aging of receivables mainly

200
http://-/?-

7/29/2019 120-748-1-PB[1].pdf

3/14


affect the financial criterion. Several financial and non-financial success factors are identified and reviewed inthis study.

3.1 Success Factors in Construction Orga-

nizations

According to Jaselskis et al. (1996), safety was consid-ered a success factor to achieve excellence in construc-tion. Abraham (2002) adopted the approach of Pol-lalis and Frieze (1993) of combining the latest strate-gic management theory: the seven guiding principlesof strategic management for civil engineering (Chi-nowsky and Meredith 2000) with the latest success fac-tors methodology for IT organizations (Rockart et al.1996). Abraham (2002) added a third dimension andincorporated information from organizational behaviortheory, specifically the characteristics of an organiza-

tion, with the two knowledge domains.

Camp (1995) provided a list of the most impor-tant internal business processes that might be consid-ered when evaluating firms performance against othercompetitors. The list includes: market management,product design and engineering, product operations,customer engagement, logistics and inventory manage-ment, product maintenance, business management, in-formation and technology management, financial man-agement, and human resource management. The firstsix factors are considered operational business pro-cesses while the rest are support business processes.

In practice, managers are encouraged to select mea-sures from three categories: customer, internal businessprocesses and learning and growth in order to assessorganization performance (Kaplan and Norton 1996).

In the construction industry, performance indicatorsare used to establish a new conceptual framework forperformance management in construction (Kagioglouet al. 2001), to conduct performance measurement ofconstruction firms in developing countries (Luu et al.

2008), and to measure proper performance in construc-tion (Bassioni et al. 2004; Bassioni et al. 2005).

According to Abraham (2002), a proposed list of suc-cess factors for construction organizations was devel-oped from the characteristics of organizations, organi-zational behavior theory, and the seven guiding prin-ciples of strategic management utilizing the Pollalisand Frieze (1993) approach for the IT organizations.The methodology followed by Pollalis and Frieze (1993)was quantitative (survey-based research) while Rockartet al. (1996) and Martin (1997) used a qualitativemethodology (interviews with top executives in the in-dustry). The objective of quantitative methodology

was to isolate categories before the study was under-taken as precisely as possible. However, the qualita-tive one depicted the nature and the definition of cat-egories in the course of a project McCracken (1988).Then, Abraham (2002) combined the two methodolo-gies together in order to benefit and capture the ad-vantages of both.

Chinowsky (2001) identified seven guiding principlesof strategic management for civil engineering industryaccording to the results of interviews with civil engi-neering, construction, and public agency executives;which included Vision, Mission, Goals, Core Com-

petencies, Knowledge Resources, Education, Finance,Markets, and Competition. The list was not inclusivefor all construction organizations; however, organiza-tions should consider continuous updates to success

Table 1. Suggested success factors for construction originations and sample of the collected raw data

Category Success Factors Responses (Scale: 1-5)

Sample #1 Sample #2

Administrative 1. Clear Vision, Mission and Goals 5 5

and Legal 2. Competition Strategy 3 53. Organizational Structure 5 5

4. Political Conditions 4 45. Number of Full Time Employees 5 5Technical 6. Usage of International Aspects (ISO) 3 4

7. Availability of knowledge 4 48. Usage of IT 5 59. Business Experience (no. of years) 4 410. Product Maintenance 2 3

Management 11. Employee Culture Environment 5 412. Employee Compensation and Motivation 5 413. Applying Total Quality Management 3 414. Training 3 4

Market and Finance 15. Quick Liquid Assets 3 416. Feedback Evaluation 4 417. Research and Development 5 5

18. Market Conditions/Customer Engagement 5 5Overall Company Performance (%) 70 80

201
http://-/?-

7/29/2019 120-748-1-PB[1].pdf

4/14


Figure 1. Typical example of a simple ANN architecture

factors as they can be changed with time according toenvironmental issues, transformation of industry, andvariations in competitive strategy. Abraham (2002) re-

vealed eleven success factors for construction organi-zations. These factors comprised: competitive strat-egy, market conditions, political environment, organi-zational structure, technical applications, employee en-hancements, process benchmarking, feedback and eval-uation, inter organizational relationships, environmen-tal factors, and management skills and relationships.Based on the above review of literature and focusingon construction organizations, the most related successfactors to construction industry are selected as shownin Table 1. These factors are considered in the presentstudy.

3.2 Artificial Neural Networks

Artificial Neural Network (ANN) attempts to modelthe brain learning, thinking, storage, and retrieval ofinformation, as well as associative recognition. TheANN consists of neurons usually organized into a se-quence of layers with full or partial connections amongsuccessive layers (Moselhi et al. 1991). Figure 1 showstypical three-layer network architecture. Similar to thehuman thinking and decision making, an ANN takespreviously trained problems to build a system of neu-rons that makes new decisions, classifications and fore-

casts. The ANN can learn patterns that are being pre-sented during the training or learning phase. Through-out the course of training, ANN develops the abilityto generalize, which correctly classify new patterns orto make forecasts and predictions (Zayed and Halpin2005b).

Back Propagation Neural Networks (BPNNs) are oneof the most common ANN algorithms, as they are sim-ple and effective and have found home in a wide assort-ment of machine learning applications. BPNNs startas a network of nodes arranged in three layers; input,hidden, and output (Zayed et al. 2005). The input andoutput layers serve as nodes to buffer input and out-

put for the model, respectively, while the hidden layerprovides a means for input relations to be representedin the output. Before any data has been run through

the network, the weights for nodes are generated ran-domly. BPNNs support a contribution factor modulewhich produces a number for each input parameter

called a contribution factor that is a rough measure ofthe importance of such variable in predicting networksoutput, relative to the other input parameters in thesame network. The higher the number, the more thevariable is contributing to prediction or classification.The ANN analysis considers the non-linear interactionrelationship among input variables (Zayed and Halpin2005a).

Based upon the above literature review, it is noticedthat assessing the construction organization from non-financial perspectives does not receive much attentionfrom researchers in the field. Most research focusedon the performance in the project level not the organi-zation level leaving a research gap in this area, whichpave the way to the present research objectives.

4 RESEARCH METHODOLOGY

To achieve the objectives of the present research, sev-eral steps are accomplished as shown in the schematicdiagram (Figure 2). These steps are summarized asfollows:

1. Review success factors in general and specify the

most common used ones for construction indus-try at the organizational level. Data regardingsuccess factors and organization performance arecollected from literature, practitioners, and vari-ous construction companies.

2. Determine the most significant factors (i.e., crit-ical) that contribute mostly to the organizationperformance through the opinion of experts inthe construction industry who are working in toporganizational level. Thus, critical success fac-tors (CSFs) are identified and their effect on thesuccess of construction organization is qualita-tively/quantitatively studied and analyzed.

3. Develop two organizational performance predic-tors (models) based on the CSFs using both ANNand regression analyses. These analysis methods

202
http://-/?-

7/29/2019 120-748-1-PB[1].pdf

5/14


Figure 2. Schematic diagram of research methodology

are selected because they are data oriented tech-niques that require the values of input and out-put variables to be available in order to accom-modate their success in predicting organizationperformance. The collected data include the val-ues of input and output variables. These data arethen prepared to be ready for ANN and regres-sion analyses. Organizational performance mod-els are then developed and ready for testing.

4. Both models are tested and verified in order tocheck their robustness in assessing the perfor-mance of a construction organization. Sensitivityanalysis is conducted to test the effect of chang-ing the input variables on the organization per-formance.

5 DATA COLLECTION

After identifying the potential success factors that mayaffect the organization performance, a questionnairewas prepared to assess the effect of each factor on

performance. These factors are collected from variousliteratures and from experts and practitioners. Thefactors are perceived by top managers to mostly af-

fect construction organization. The questionnaire wasdesigned to identify factors that affect company per-formance and then, to predict this performance in anabstract approach. It had two parts where the firstpart (I) was asking decision makers in construction or-ganizations to fill-in questionnaires reflecting their ex-perience and company information. Part II was askingthe experts, using a specified 5 point subjective scale asshown in Figure 3, to assess the effect of identified suc-

cess factors on organization performance. In addition,the decision-maker was asked to assess the overall suc-cess of his/her construction organization using a valueout of 100. One hundred and fifty questionnaires weresent to top and middle management decision makersin construction organizations worldwide, i.e., Canada,Egypt, France, Greece, Germany, USA, Saudi Arabiaand United Arab Emirates. The response rate wasimmense because sixty three responses were receivedwith a response rate of 42%, which was sound in theconstruction industry. They were scattered as follows:Egypt (31 questionnaires), Canada (21 questionnaires),and other countries (11 questionnaires). Sample of the

raw data of two responses obtained from the question-naire is given in Table 1. It is noticed from these tworesponses that several factors are agreed upon to be the

203
http://-/?-

7/29/2019 120-748-1-PB[1].pdf

6/14


Figure 3. Performance scale for success factors

most significant ones, such as Clear Vision, Mission &Goals, Structure, No. of Employee, Usage of IT,Research & Development , and Market Conditions.It is also noted that the experts evaluation for thesetwo companies are 70 and 80 out of 100 for sample 1and 2, respectively. These values are assessed basedupon the given criteria and factors as well as expertsjudgment.

Given the diversity of cultures present in each ofthese countries, the manner with which they make de-cisions and run their business differs, as does their eval-uation of these aspects. However, due to the scarcity

of available data and to the lack of information relatedto companies internal records, the present study doesnot consider the diversity. This assumption can betreated vigorously in future studies where researchers

might depend on the results of the present study to fo-cus on the most critical factors. Therefore, the presentstudy will be considered as an overview, an outline, ora framework that guides future researchers to the start-ing point(s) of detailed research. In other words, thepresent research assists in developing an overall frame-work/roadmap for research in this area in an abstractapproach where detailed research is however requiredto study each CSF and its effect on organization per-formance.

6 DETERMINATION OF CRITICALSUCCESS FACTORS

There are several methods to assess the significanceof independent factors affecting the performance of a

Table 2. Ranking and relative significance of success factors

Rank Success Factors Contribution Difference in

Percentage Contribution Percentage

1* X1: Availability of knowledge 7.13

2* X2: Clear Vision, Mission & Goals 7.02 0.1143* X3: Organizational Structures 6.73 0.282

4* X4: Feedback Evaluations 6.63 0.1015* X5: Business Experience 6.55 0.0826* X6: Political Conditions 6.48 0.077* X7: Research & Development 6.45 0.0298* X8: Employee Culture Environments 6.25 0.1999* X9: Competition Strategy 6.14 0.11710 Market Conditions/Customer Engagement 5.34 0.79411 Training 5.12 0.22512 No of Full Time Employees 5.01 0.10513 Product Maintenance 4.82 0.19314 Usage of IT 4.75 0.0715 Quick Liquid Assets 4.37 0.37816 Applying TQM 4.06 0.30817 Usage of International Aspects (ISO) 3.99 0.07

18 Employee Compensation and Motivation 2.79 1.199

* Critical Success Factors (CSF).

204
http://-/?-

7/29/2019 120-748-1-PB[1].pdf

7/14


dependent criterion. In the present research, ANN isused to assess the most significant success factors usingNeuroshell software package. This is because the ANNsoftware provides the contributing weight of each fac-tor after completing the training process. Therefore,

in the following sub-sections, details regarding thesecontributing weights are presented.

6.1 Artificial Neural Network Training

Before the ANN is trained, training criteria must bespecified in advance, which include maximum and min-imum absolute errors and number of training cycleswithout improvements. The data space is divided intothree sets: training, test, and production. The trainingset is used to train the network where the error with re-spect to the training cycles is calculated. The test set isused to test the network during development/training

and to continuously correct it by adjusting the weightsof network links in order to reduce the error. The pro-duction set is the part of data which is used to validatethe model(s).

6.2 Ranking of Input variables

The purpose of establishing a rank of input variablesis to determine the relative importance of the vari-ables and to identify those mostly affect organizationalperformance. In this study, estimates of the contri-bution of eighteen input (independent) variables aredone. The contribution percentages are derived from

the analysis of weights generated from the trainedANN. The higher the number, the more that variableis contributing to the classification and/or prediction.Evidently, if a certain variable is highly correlated, the

variable will have a high contribution percentage. InTable 2, the contribution percentage (relative signifi-cance) of the 18 success factors are given. The successfactors are ranked according to their contribution per-centages. The differences in contribution percentage

between each two successive success factors are alsogiven in Table 2. It is obvious that the difference incontribution percent between success factors No. 9 and10 is 0.794 which is about seven times the immediatepreceding difference in contribution percent (0.117).Therefore, only the first nine factors can be consid-ered more significant (i.e., critical) factors. These nineCSFs are utilized to develop two models that predictthe overall performance of a construction organization.

7 DEVELOPMENT OFORGANIZATION PERFORMANCE

MODELS

Both ANN and regression analysis are used to developtwo prediction models for the performance of construc-tion organizations. The two models are developedbased on the previously specified/selected CSFs. Theyare developed in order to select the best model thatmight represent the collected data.

7.1 ANN-Based Organization PerformanceModel

Data on the selected CSFs are only used to train a new

ANN in order to obtain an ANN-based organizationperformance model. Similar training criteria are used(i.e., maximum and minimum absolute errors and num-ber of training cycles without improvements). Data

Figure 4. The ANN architecture for the developed model

205
http://-/?-

7/29/2019 120-748-1-PB[1].pdf

8/14


are divided into two randomly selected sets: training(84%) and validation (16%). The training data set isused to train the network, using Neuroshell softwarepackage, while validation data set is used to test theoutput of the trained models response to the inputs of

this data set. In other words, the inputs of validationdata set cases are introduced to the trained model inorder to generate the predicted output. This outputis then compared to the actual collected output valuesfrom experts. If they are similar or approximate, thenthe model is valid and vice versa.

The selection of input and output variables greatlyaffects the ANN architecture. The selection of thesevariables depends basically upon the nature of theproblem. As shown in Figure 4, the input variablesinclude the previously selected CSFs, which are 9 mainCSFs. Each variable is represented by one artificialneuron in the networks input layer. The ANN has only

one output neuron that represents the performance ofa construction organization. Hence, to build the ANNarchitecture, there should be nine neurons in the inputlayer and one neuron in the output layer. The hiddenlayer relies on the available model building data setand the nature of outputs. Several iterations are usedto generate the optimal number of neurons in the hid-den layer. The ANN achieves the required goal (0.05)when the training network has 30 neurons in the hid-den layer, learning rate of 0.05, and 2000 number ofepochs. Therefore, the best architecture for ANN is9 input, 30 hidden, and 1 output neurons. After the

ANN is trained, it can be recalled to predict the orga-nization performance for any given values of the CSFs.The training and testing processes are performed suc-cessfully with reasonable results in which the R2 ofthe ANN model equals to 0.81, the mean square error(MSE) equals to 0.147, and the mean absolute errorequals to 0.196. These results show the robustness ofthe developed model using real world data, which aretypically noisy.

7.2 Regression-Based Organization Perfor-mance Model

MINITAB is used to build a regression model for con-struction organizations performance as a function ofthe previously selected CSFs. MINITAB is a general

purpose statistical package which provides a wide rangeof basic and advanced data analysis capabilities, suchas analysis of variance, basic statistics, correlation andregression, and multivariate analysis (MINITAB 2006).Step-wise regression analysis is utilized to select thebest number of variables to be included in the model.Four selection criteria are used to distinguish betweendifferent proposed models. These criteria are R-square,adjusted R-square, MSE, and Mallows Cp. The bestmodel that can represent the collected data set is se-lected according to the largest R-square and adjustedR-square, the minimum MSE, and the closest Cp to

the number of independent variables. Hence, if the se-lected model has only five independent variables, thebest model is the model with Cp value close to five. Allthese criteria have been considered when selecting thebest model to achieve the above requirements. There-fore, the selected model has the highest R2 of 0.79 andadjust R2 of 0.783, the Cp value of 8.2 close to 9 (i.e.,number of variables), and the minimum MSE value of2.2524. Organizational performance [Y] is measured asthe actual output of an organization measured againstits intended/planned outputs (i.e., goals and objec-tives). The best obtained formula describing the or-ganization performance [Y(%)] as a function of CSFs

is given by Eq. (1):

Y(%) = 62.7 + 1.93X1 + 4.33X2

2.02X3 + 1.19X4 + 3.83X5

3.87X6 + 1.83X7 + 1.24X8

2.68X9

(1)

The dependent variable (Y) denotes the organizationperformance expressed as a percentage and Xs denote

Table 3. Analysis of variance for the developed regression model

Source DF SS MS F P

Regression 9 9550.4 1061.2 52.29 0.000Residual Error 263 5337.3 20.3

Total 272 14887.6

Factor X Predictor Coefficient P-value

1 Availability of knowledge 1.9332 0

2 Clear Vision, Mission & Goals 4.3319 0.0283 Organizational Structure -2.0214 0.6864 Feedback Evaluation 1.1871 05 Business Experience 3.8339 06 Political Conditions -3.8647 07 Research & Development 1.8312 0

8 Employee Culture Environment 1.2373 0.6949 Competition Strategy -2.6753 0

206
http://-/?-

7/29/2019 120-748-1-PB[1].pdf

9/14


the CSFs specified in Table 3 and the subscript refers totheir numbers in the table. For example, X7 denotesCSF number 7 which is Research & Development.The developed model is checked for their statistical va-lidity. The main diagnostics in this regard are R2 (co-

efficient of multiple determination), F-test, and t-testfor model coefficients. The R2 measures the propor-tional variation in company performance explained bythe nine CSFs. The coefficient of multiple determina-tions (R2) is the most commonly used measure, whichapplies to multiple regression analysis. A perfect fitwould result in an R2 value of 1, a very good fit near 1,and a very poor fit near 0. The R2 value correspondingto Eq. (1) is 0.79, which shows a good degree of to-tal variability considering the noisy in real world data.The R2 adjusted (0.783) accounts for the number ofpredictors in the model. Both values indicate that themodel fits the data well.

To determine P(F) for the entire model, a hypoth-esis test is carried out. The null hypothesis (H0) as-sumes that all regression coefficients, 0, 1 ... p1equal to zero, i.e., 0 = 1 = p1 = 0 and the al-ternate hypothesis (Ha) assumes that not all of themequal to zero. Based on the Minitabs output, shownin Table 3, the P-value for the F-test is 0.00, whichmeans that the null hypothesis is rejected. Similarly,to determine the validity of regression coefficient in-dividually, t-tests are performed separately for theindividual coefficients: 0, 1 ... p1. In case of0,the null hypothesis (H0) oft-test assumes that 0 = 0;

while alternative hypothesis (Ha) assumes that 0 = 0.Similarly, the other null hypothesis assumes that 1 =0 and vice versa. The results of these tests, for X1, X2,X4, X5, X7, X8, and X9 indicate that the P-value forX1 is 0.028 and 0.00 for the rest as shown in Table 3.As a result, alternative hypothesis is accepted with 95%confidence. However, the P-value for X3 and X6 are0.686 and 0.694, respectively. These results have P-values greater than 0.05, which indicates that there

could be a weak evidence of null hypothesis for theseparticular coefficients. However, due to large numberof predictors in the model and due to satisfactory re-sults of other statistical diagnostics, these results areconsidered acceptable for the model as a whole. In

other words, the entire model has robust statistical di-agnostic analysis with P-value of the F-test equals tozero. If there are several model parameters, which arenot individually significant, will not affect the predic-tion capabilities of the entire model. Attempts are con-ducted in order to remove X3 and X6 from the modeland build a new model with only seven variable; how-ever, this leads to unsatisfactory results statisticallyand logically. Therefore, the presented model is se-lected/considered although it fails the t-tests for twovariables because the entire model is sound.

Based on statistical tests, the designed regressionmodel performs well in representing current data.

Looking at the coefficients of the regression model inEq. (1), it is quite clear that the factor Clear Vi-sion, Mission & Goals has the highest positive im-pact on company performance with a coefficient valueof 4.33 followed by Business Experience with a coef-ficient value of 3.83. Conversely, Political Conditionshas the highest negative impact on organization perfor-mance with a coefficient value of -3.87 followed by thecomplexity of Organizational Structure, which has acoefficient value of -2.02. These values appear reason-able in which more details are discussed in the sensi-tivity analysis section of this paper.

7.3 Validation of Developed OrganizationalPerformance Models

The validation process is an important step to guar-antee that the developed models best fit the availabledata. All models are tested statistically, logically, andpractically to determine whether they are efficient inpredicting real world results. The collected data aredivided into two data sets, model building (80%) and

Table 4. Validation results of the developed models

Case No. Company ANN Model Regression ModelPerformance (%) Predicted AIP Predicted AIP

Performance (%) Performance (%)

1 80 82.2 0.03 88.96 0.11

2 70 74.38 0.06 75.27 0.083 87 83.23 0.04 90.01 0.034 82 82.8 0.01 87.01 0.065 90 80.17 0.11 85.82 0.056 80 82.36 0.03 85.67 0.077 75 77.54 0.03 80.71 0.088 75 82.78 0.1 82.37 0.19 75 75.89 0.01 69.8 0.07

10 80 75.02 0.06 77.33 0.03

AIP (%) = 4.94 AIP (%) = 6.77AVP (%) 94.24 AVP (%) 93.23

207
http://-/?-

7/29/2019 120-748-1-PB[1].pdf

10/14


Table 5. Summary of sensitivity analysis results for the developed models

No. Critical Success Factor ANN Model Performance (%) Regression Model Performance (%)

Min. Max. Range Min. Max. Rangevalue value value value

(1) (2) (3)=(2)-(1) (4) (5) (6)=(5)-(4)

1 X1: Availability of knowledge 81.08 84.15 3.07 74.25 83.9 9.652 X2: Clear Vision, Mission & Goals 79.89 91 11.11 67.05 88.7 21.653 X3: Organizational Structures 87.33 91.45 4.13 76 86.1 10.14 X4: Feedback Evaluations 78.82 87.67 8.85 76.47 82.42 5.955 X5: Business Experience 81.02 85.56 4.54 68.55 87.7 19.156 X6: Political Conditions 77.95 79.03 1.08 72.3 91.65 19.357 X7: Research & Development 81.79 87.87 6.08 74.55 83.7 9.158 X8: Employee Culture Environments 76.32 86.23 9.91 76.32 82.52 6.29 X9: Competition Strategy 73.34 74.98 1.64 74.68 88.08 13.4

validation (20%). The validation data set, which is se-lected randomly, is kept away while modeling the ANNand regression analysis. After building both models,the validation data set is utilized to test the abilityof the developed models to predict the organizationperformance. The developed models are validated bycomparing the predicted results with the actual valuesof the validation data set. Eqs. (2) and (3) are used tovalidate the developed models (Al-Barqawi and Zayed2006) as follows:

AIP =

n

i=1

[1 (Ei/Ci)] 100/n (2)

AV P = 100AIP (3)

where AIP is the Average Invalidity Percent, AVP isthe Average Validity Percent, Ei is the ith predictedvalue, Ci is the ith actual value, and n is the numberof observations.

Eq. (2) expresses the average invalidity, which indi-cates the prediction error, while Eq. (3) presents theaverage validity percent. The AVP values for the de-veloped performance prediction models using the ANNand regression are 94.24% and 93.23% as shown in Ta-ble 4, respectively. These values indicate that the ob-

tained results are satisfactory. Table 4 also shows thatthe prediction of ANN model is closer to the actualdata than that of regression model outputs. This isbecause the ANN model predicts the performance in 6cases (i.e., organization numbers 1, 2, 4, 6, 7, and 9)closer to the actual performance than regression modeloutputs. However, the regression model predicts theperformance of only 3 cases (i.e., organization num-bers 3, 5, and 10) closer to the actual performance thanthe ANN model outputs. There are few organizationswhere the ANN model predicts the performance withdiscrepancy in which AIP equals to 11% and 10% fororganization numbers 5 and 8, respectively. The case is

similar in the regression model where it has discrepancyof AIP equals to 11% and 10% for organization num-bers 1 and 8, respectively. Comparing the results of

both models, it is quite clear that they are comparable;however, the ANN model is more precise and generatesbetter results, i.e., closer to real cases. Therefore, theANN model is utilized to draw the performance curvesin Figures 5 and 6 based upon sensitivity analysis re-sults.

7.4 Sensitivity Analysis

To test the effect of each CSF on the organization per-formance, a sensitivity analysis is performed using thedeveloped performance prediction models. Each factoris allowed to change within its scale limits (1-5) fol-lowed by predicting the organization performance using

the developed models. The maximum and minimumvalues as well as the range of change in organizationperformance corresponding to the change in each CSFare given in Table 5. It is obvious form Table 5 thatthe organization performance is greatly affected by thechange in the value of CSF number 2 (Clear Vision,Mission & Goals) for both ANN (range = 11.11%) andregression (range = 21.65%) models because it has themaximum range. On the other hand, the CSFs, whichhave the least effect on organization performance in-clude CSF number 6 (Political Conditions) in ANNmodel (range = 1.08%) and CSF number 8 (FeedbackEvaluations) in regression model (range = 5.95%).

Based upon the sensitivity analysis of ANN model,performance curves are developed in order to measurethe variation of the company performance versus thevarious CSFs as shown in Figures 5 and 6. Figure 5shows the curves of various CSFs, which are directlyrelated to company performance, i.e., the more theCSF value, the more the performance will be. Onthe other hand, Figure 6 shows the CSF, which areinversely related to the company performance. For ex-ample, Figure 5a shows the effect of CSF X1 (Availabil-ity of Knowledge) on company organization. It shows apositive effect in which the performance equals almost

84% when the value of knowledge equals 2 (moderatelyineffective) and 87% when the X1 value is 4 (moder-ately effective). The developed curves in Figure 5 show

208
http://-/?-

7/29/2019 120-748-1-PB[1].pdf

11/14


Figure 5. Factors that are directly related to company performance (positive slope)

robust equations with R2 values above 95%. Similarly,Figure 6a shows the effect of CSF X3 (OrganizationalStructures) on company organization. It shows a nega-tive effect in which the performance equals almost 89%when the value of knowledge equals 2 and 81% whenthe X3 value is 4. The developed curves in Figure 6show robust equations with R2 values above 98%.

The developed curves show that organization per-

formance is directly related to Availability of Knowl-edge, Clear Vision, Mission & Goals, Feedback Eval-uation, Business Experience, Research & Devel-

opment, and Employee Culture Environment. Itmakes sense that the organization performance in-creases when increasing, for example, the availabilityof knowledge to organizations employees or system.It also makes sense to increase performance when thecompanys vision, mission, and goals are very clear toall management levels and employees. Similarly, theorganization performance increases by increasing ex-

perience, percentage of research and development, andcontinuous accommodation of the feedback from em-ployees and customers. On the contrary, the organi-

209
http://-/?-

7/29/2019 120-748-1-PB[1].pdf

12/14


Figure 6. Factors that are inversely related to company performance (negative slope)

zation performance is inversely related to Organiza-tional Structure, Political Conditions, and Compe-tition Strategy. This also makes sense because if anorganization accommodates a complex structure, at-tains unstable political conditions, and has many com-petitors or defective strategy, this negatively impactsits performance. Therefore, based on the aforemen-tioned tests and discussions, the developed curves arestatistically and logically sound.

Based upon the above discussion, it is concluded thatthe developed models are robust, realistic, and prac-tical in predicting construction organization perfor-mance and its trend towards various CSF. The CSF canbe used as indicators of the organization performanceand the developed charts in Figures 5 and 6 can be uti-lized as gauges for performance improvement/drop. Inaddition, the developed models can be implemented asa conceptual assessment tool for the entire performanceof a company, then, detailed investigation is warrantedwhen needed. More details of the critical success fac-tors as well as extra factors should be vigorously re-searched and analyzed in future research activities on

organization performance. The developed models aredeemed essential to top management of constructionorganizations given that they provide them with the

means to assess their organizations performance andmeasure/gauge how to improve such performance.

8 STUDY LIMITATIONS

The presented study in this paper comprises severallimitations as follows:

1. Only nine main factors are considered when de-

veloping the intended models to predict the over-all performance of a construction organization.Many other sub-factors are not also consideredin this study.

2. Construction organization characteristics andstructures are not considered in this study as itis in the exploration stage of this area.

3. The collected data rely only on experts opin-ion without considering quantitative data relatedto the performance of construction organization.This mainly results from the scarcity of availabledata and confidentiality of such data.

4. The risk for bias is not measured nor considered

when dealing with experts who are evaluatingtheir organization. The experts might be pro oragainst their organization which will affect their

210
http://-/?-

7/29/2019 120-748-1-PB[1].pdf

13/14


assessment. This bias is not treated in the cur-rent study.

The abovementioned limitations will be consideredfor future studies in order to improve the developedperformance assessment models. Therefore, the pre-

sented study is only considered as a discovery for thearea of construction organization performance, its lim-itation, research gaps, difficulties, and problems thatneed solutions.

9 SUMMARY AND CONCLUSIONS

Construction is a highly competitive industry nowa-days. Achieving success depends on many factorswhich affect the performance of construction organiza-tions. It is difficult to measure the non-financial perfor-mance of construction organizations due to their diver-

sity and complexity. A multi-dimensional study on per-formance of construction organizations has been con-ducted using sixty three surveys obtained from variousconstruction organizations worldwide. The collecteddata are analyzed using ANN to determine the relativesignificance of various success criteria (i.e., critical suc-cess factors). These specified CSFs are used in turn todevelop ANN and regression-based performance mod-els in order to predict the performance of constructionorganizations. The developed models are validated andcompared using the average validity percent where theresults found were satisfactory, i.e., AVP = 94.24%

and 93.23% for ANN and regression models, respec-tively. The developed models benefit both researchersand practitioners because they provide academics withrobust ANN and regression models or frameworks thatadvance the state of the art of predicting constructionorganization performance. They also provide practi-tioners with the various essential factors affecting or-ganization performance and the techniques with whichit can be assessed or predicted, i.e., performance as-sessment and prediction tools.

REFERENCES

Abraham, G. (2002). Identification of Critical SuccessFactors for Construction Organizations in the Archi-tectural/Engineering/Construction (A/E/C) Indus-try. Ph.D. Thesis, Georgia Institute of Technology,Atlanta, United States.

Ahmad, I. and Dye, J. (1994). Development of aDatabase of MBE/DBE Firms and Decision Modelto Determine their Capacity for the Florida Con-struction Industry. Technical Report No. 120, De-partment of Construction Management, Florida In-ternational University, United States.

Al-Barqawi, H. and Zayed, T. (2006). Condition rat-

ing model for underground infrastructure sustain-able water mains. Journal of Performance of Con-structed Facilities, 20(2), 126135.

Aldrich, H. (1979). Organizations and Environments.Organization Studies, Englewood Cliffs, New Jersey,United States.

American Heritage College Dictionary (1993).Houghton Miffin Organization. Boston, United

States.Bassioni, H., Price, A., and Hassan, T. (2004). Per-formance measurement in construction. Journal ofManagement in Engineering, 20(2), 4250.

Bassioni, H., Price, A., and Hassan, T. (2005). Build-ing a conceptual framework for measuring businessperformance in construction: An empirical evalu-ation. Construction Management and Economics,23(5), 495507.

Caballero, A. A. and Dye, J. (1999). Comparison ofconstruction firms based on fuzzy sets. Journal ofConstruction Education, 4(3), 313320.

Camp, R. (1995). Business Process Benchmarking:

Finding and Implementing Best Practices,. ASQCQuality Press, Milwaukee, Wisconsin, United States.

Chinowsky, P. (2001). Strategic management in en-gineering organizations. Journal of Management inEngineering, 17(2), 6068.

Chinowsky, P. and Meredith, J. (2000). Strategic Cor-porate Management for Engineering. Oxford Univer-sity Press, New York, United States.

Daniel, D. (1961). Management information crisis.Harvard Business Review, 39(5), 111116.

Hawawini, G., Subramanian, V., and Verdina, P.(2004). The home country in the age of global-

ization: How much does it matter for firm perfor-mance. Journal of World Business, 39(2), 121135.Holophan, J. (1992). Use of executive information sys-

tem in measuring business performance. Journal ofInformation Technology, 7(3), 177186.

Isik, Z., Arditi, D., Dikmen, I., and Birgonul, M. T.(2010). Impact of resources and strategies on con-struction company performance. Journal of Man-agement in Engineering, 26(1), 918.

Jaselskis, E. J., Anderson, S. D., and Russell, J. S.(1996). Strategies for achieving excellence in con-struction safety performance. Journal of Construc-tion Engineering and Management, 122(1), 6170.

Kagioglou, M., Cooper, R., and Aouad, G. (2001).Performance management in construction: A con-ceptual framework. Construction Management andEconomics, 19(1), 8595.

Kaplan, R. S. and Norton, D. P. (1993). Putting thebalanced scoreboard to work. Harvard Business Re-view, 71(5), 3440.

Kaplan, R. S. and Norton, D. P. (1996). Using the bal-anced scorecard as a strategic management system.Harvard Business Review, 74(1), 7585.

Luu, T., Kim, S., Cao, H., and Park, Y. (2008). Per-formance measurement of construction firms in de-

veloping countries. Construction Management andEconomics, 26(4), 373386.

Martin, R. (1997). Do we practice quality principles

211
http://-/?-

7/29/2019 120-748-1-PB[1].pdf

14/14


in the performance measurement of critical successfactors. Journal of Total Quality Management, 8(6),428439.

McCracken, G. (1988). The Long Interview. Qualita-tive Research Methods Series, Volume 13, Sage Pub-

lications, Newbury Park, California, United States.MINITAB (2006). Users Manual. Release 14, MintabInc., State College, Pennsylvania, United States.

Moselhi, O., Hegazy, T., and Fazio, P. (1991). Neu-ral networks as tools in construction. Journal ofConstruction Engineering and Management, 117(4),606625.

Parfit, M. and Sanvido, V. (1993). Checklist on criti-cal success factors for building projects. Journal ofManagement in Engineering, 9(3), 243249.

Pollalis, Y. and Frieze, I. (1993). A new look at criti-cal success factors in IT. Information Strategy, TheExecutives Journal, 10(1), 2435.

Richard, A. (2004). The Critical Success FactorMethod: Establishing a Foundation for EnterpriseSecurity Management. Technical Report, SoftwareEngineering Institute, Carnegie Mellon University,Pennsylvania, United States.

Rockart, J. (1979). Chief executives define their owndata needs. Harvard Business Review, 38(1), 4355.

Rockart, J. (1982). The changing role of informationsystem executive: A critical success factors perspec-

tive. Sloan Management Review, 24(1), 313.Rockart, J. and Bullen, C. (1981). A Primer on Critical

Success Factors. Center for Information Systems Re-search, Massachusetts Institute of Technology, Mary-land, United States.

Rockart, J., Earl, M., and Ross, J. (1996). Eight im-peratives for the new IT organization. Sloan Man-agement Review, 38(1), 4355.

Shurchuluu, P. (2002). National productivity and com-petitive strategies for the new millennium. Inte-grated Manufacturing Systems, 13(6), 408414.

Wethyavivorn, P., Charoenngam, C., and Teerajet-gul, W. (2009). Strategic assets driving organiza-tional capabilities of thai construction firms. Jour-nal of Construction Engineering and Management,135(11), 12221231.

Zayed, T. and Halpin, D. (2005a). Pile constructionproductivity assessment. Journal of Construction

Engineering and Management, 131(6), 705714.Zayed, T. and Halpin, D. (2005b). Productivity and

cost regression models for pile construction. Jour-nal of Construction Engineering and Management,131(7), 779789.

Zayed, T., Halpin, D., and Basha, I. (2005). Produc-tivity and delays assessment for concrete batch plant-truck mixer operations. Construction Managementand Economics, 23(8), 839850.

212
http://-/?-

Documents

120-748-1-PB[1].pdf