INVESTIGATION AND MODELING OF CRITICAL SUCCESS FACTORS IN CONSTRUCTION ORGANIZATIONS

INVESTIGATION AND MODELING OF CRITICAL SUCCESS FACTORS IN CONSTRUCTION ORGANIZATIONS Emad Elwakil 1, Mohammad Ammar 2, Tarek Zayed 3, Muhammad Mahmoud 4, Ahmed Eweda 1 and Ibarhim Mashhour 4 1 PhD Student, Concordia University, Department of Building, Civil & Environ. Eng., 1455 De Maisonneuve Blvd. West, Montreal, Quebec, Canada H3G 1M8. 2 Associate Professor, Structural Engineering Dept., Faculty of Engineering, Tanta University, Tanta, Egypt. Presently: Visiting Scholar, BCEE Department, Concordia University, Montreal, Quebec, Canada. mamammr@yahoo.com. 3 Associate Professor, Building, Civil, and Environmental Engineering Department, Concordia University, Montreal, Quebec, Canada, email: zayed@encs.concordia.ca 4 M.A.Sc Student, Concordia University, Department of Building, Civil & Environ. Eng., 1455 De Maisonneuve Blvd. West, Montreal, Quebec, Canada H3G 1M8. ABSTRACT Profit and success are considered the main drivers of any organization. Achieving this success is based on many factors which have a direct effect on the performance of these organizations. Predicting construction organizations performance helps define the weak organization points in order to improve its performance and increase the profit. In construction organizations, it is more difficult to achieve or maintain a scientific strategy to measure their current success due to the diversity and complexity of construction organizations. Previous studies used questionnaires and interviews with technical and professional persons. However, most of these studies concentrated on the critical success factors on project level. The scope of this study is to investigate the most significant organizational success factors with focus on construction organizations. This paper aims at determining most significant (i.e. critical) success factors, and to develop a model to predict the company performance based on these critical success factors. The potential success factors were surveyed from the literature study. A questionnaire was prepared for evaluating the effect of those potential success factors on organizational performance. The data collected were analyzed using Artificial Neural Networks (ANNs). Neuro-Shell software was used to rank the potential success factors utilizing the data obtained from different construction organizations. The critical success factors were used in-turn to develop a NN prediction performance model of construction organizations. The model can be used to predict the performance of a construction organization based on estimated values of its success factors. 350

351 INTRODUCTION Currently, the world is witnessing construction booming. Profit and success are considered the main drivers of any organization. Achieving success is usually based on many factors which have direct effect on the performance of organizations. In construction organizations, it is more difficult to achieve or maintain a scientific strategy to measure their current success due to the diversity and complexity of construction organizations (Abraham 2002). Predicting performance of construction organizations helps define weak points in order to improve its performance and increase the profit. Kaplan and Norton (1993) concluded that it is critical to measure and control new strategies after they have been put to work at the operational phase of construction organizations. In a research conducted to measure the performance on executive monitoring system, Holophan (1992) proved that critical success factors (CSFs) are the best methodology to develop an executive monitoring system to contain corporate-wide indicators of success. Success is the main aim of any organization. Many research efforts have been done to determine the success factors but most of these studies have been done on the project level not on the organizational level. The determination of CSFs was approached via various methods. The most commonly used methods are questionnaires and interviews with technical and professionals. Most of the construction organizations CSFs are qualitative rather than quantitative. To reach construction organization success, it was found that there is a need to determine these factors and determine the critical ones at the organizational level. A model can be build to relate organization success with CSFs, which can then be used later to predict and improve organizational performance. The scope of the present study is to investigate the most significant organizational success factors with focus on construction organizations. The main objective is to specify CSFs after prioritizing potential success factors and determining their impact on the performance of construction organizations. This study went through the following organization. Literature review of critical success factors and previous modeling work was carried-out. Extensive efforts were done to collect data from various construction organizations. Data analysis was performed using artificial neural network technique. The neural network technique was used to rank potential success factors covered in the questionnaire. A regression-based model to predict organizational performance was developed. The proposed model was verified and then validated using R 2 statistical measure. LITERATURE REVIEW Organization as a term has many different definitions. It is defined according to the American Heritage College Dictionary (1993) as: An organization is a structure through which individuals cooperate systematically to conduct business. Aldrich (1979) defined the organization as: Goal directed boundary-maintaining activity systems. The definition of success has been changed and more developed to include the quality of the project as an indication of success. The project success differs from one person to another and the success criteria are changing according to the project itself. However, success is mostly defined as the overall achievement of a project goals and expectations (Parfit and Sanvido 1993). Manufacturing, communication

352 and other industries used the revenue growth rate measurement over a period of time as a factor to determine success. The previous definitions of success are project oriented. Moreover, they are not easily measurable whether they are quantitative or qualitative factors. CSFs as a concept and their applications to business are not new; it dates back to 1960s by Daniel (1961). CSFs appeared first in information system industry to identify executive information needs (Rockart 1979). Rockart (1981) approached and described the principles of success factors to make a systematic way of identifying the needed information for executives. Rockart (1982) defined CSFs as the limited number of areas in which satisfactory results will ensure successive competitive performance of the organization. Rockart and Bullen (1981) differentiated between the goals and CSFs through the traditional strategic planning and management, where the goal and objectives are fairly well known but the CSFs definition is much less clear. However, the organization goals are defined to be the targets that are established to achieve the organization s mission. They are very specific, known what and when to be achieved, by whom and should have a quantitative measurable elements (Richard 2004). Rockart (1979) identified four prime sources of CSFs for any organization working in any industry, which are: 1. Structure of the industry: has its own set of CSFs which are dependent on its characteristics. 2. Competitive strategy, industry position and geographic location: Each organization has its own strategies and strategic plan due to the nature of the industry in which it operates. 3. Environmental factors: the effects of the environment upon the organization behavior are essential to understand the CSFs. 4. Temporal factors: CSFs change with the change of the organization priorities, where the areas of activity for success changes and some activities become more critical and other become less critical. Ahmed and Dye (1994) addressed the common essential attributes of business organizations throughout an extensive literature. These attributes should be taken into consideration while analyzing organization performance. These organizational performance criteria and their sub-factors are summarized in Table 1. Camp (1995) gave a list of the most important internal business processes that might be considered when evaluating firm s performance against other competitors. The list includes: Market Management, Product Design and Engineering, Product Operations, Customer Engagement, Logistics and Inventory Management, Product Maintenance, Business Management, Information and Technology Management, Financial Management, and Human Resource Management. The first six factors are considered operational business processes, while the rest are support business processes. In furniture industry, Buxey (2000) described the various strategies of Australian companies, in which the main strategies attributes are product innovation and quality, brand image, excellent customer support, outsourcing, relocation to off-shore sites to lower cost, and tariff protection. Quesada and Gazo (2007) modeled a prioritization tool for ranking CSFs and key performance measures and internal business processes in furniture organizations. As a result of Quesada and Gazo (2007)

353 study, it was concluded that the most critical internal business processes for the office furniture companies were customer engagement and product operations. Table 1: Performance Criteria and Factors (Adopted from Caballero and Dye 1999) Business Experience Personnel Financial Number of years in Number of full time Annual Revenues business Number of Contracts completed, previous 3 years employees Average length of time employed Liquid Quick Assets Largest contract completed in last 3 years Ratio of supervisors to tradesmen Dollar value of lines of credit References Level of training of supervisors Dunn and Bradstreet rating Type of License Workers Compensation Aging of receivables Modification factor Established full-time office Past studies on CSFs in the IT industry used semi-structured or structured interviews for data collection; through which the organization s key factors could be known. These efforts provided a standardized set of CSFs that can be used in different types of IT organizations. Pollais and Frieze (1993) also proposed a list of CSFs for IT organizations which was developed using strategic management concepts, specifically competitive advantage and strategic planning. CONSTRUCTION ORGANIZATIONS SUCCESS FACTORS Abraham (2002) adopted the approach of Pollais and Frieze (1993) for combining the latest strategic management theory; the seven guiding principles of strategic management for civil engineering (Chinowsky and Meredith 2000), with the latest CSFs methodology theory for IT organizations (Rochart et al. 1996). Abraham (2002) added a third dimension and incorporated information from organizational behavior theory, specifically the characteristics of an organization, with the two knowledge domains. A fuzzy-based expert system model was developed by Caballero and Dye (1999) to help individuals, agencies, or general contractors to rank and classify the capability of competing construction organizations to perform a certain project. According to Jaselskis el al. (1996), safety was considered as a performance success factor and to achieve excellence in construction. Furthermore, construction industry combined injury and illness which were rated to be the most after agriculture. According to Abraham (2002), a proposed list of CSFs for construction organizations can be developed from the characteristics of organizations, organizational behavior theory, and the seven guiding principles of strategic management utilizing the Pollais and Frieze (1993) approach for the IT organizations. The methodology followed by Pollais and Frieze (1993) was quantitative (survey-based research) while Rockart et al. (1996) and Martin (1997) used a qualitative (interviews with top executives in the industry) methodology. The quantitative methodology goal was to isolate categories before the study undertaken as precisely as possible, while the qualitative one expects the nature and the definition of categories to change in the course of a project

354 (McCracken 1988). Then, Abraham (2002) combined the two methodologies together in order to benefit and capture the advantages of both. Abraham (2002) developed the CSFs from Rockart et al. (1996) identification of eight imperatives for IT organizations and Chinowsky s (2001) identification of seven guiding principles of strategic management for civil engineering industry. The list is not inclusive in all construction organizations, but organizations should take into consideration the updates to CSFs methodology as the CSFs can be changed by time according to environmental issues, transformation of industry, and variations in competitive strategy. Abraham (2002) revealed eleven CSFs for construction organizations. These factors are: Competitive Strategy, Market Conditions, Political Environment, Organizational Structure, Technical Applications, Employee Enhancements, Process Benchmarking, Feedback and Evaluation, Inter Organizational Relationships, Environmental Factors, and Management Skills and Relationships. Chinowsky (2001) comprises seven areas according to the results of interviews with civil engineering, construction, and public agency executives; which are Vision, Mission, and Goals, Core Competencies, Knowledge Resources, Education, Finance, Markets, and Competition. Based on the literature review and focusing on construction organizations, the most potential CSFs are given in Table 2. These factors will be considered in the present study. Table 2: Potential Success Factors Choosing for the Present Study No. Success Factor No. Success Factor 1 Clear Vision, Mission and Goals 10 Usage of International Aspects (ISO) 2 Competition Strategy 11 Availability of Knowledge 3 Organizational Structure 12 Usage of IT 4 Political Conditions 13 Business Experience (no. of years) 5 No. of Full Time Employees 14 Product Maintenance 6 Employee Culture Environment 15 Quick Liquid Assets 7 Employee Compensation and 16 Feedback Evaluation Motivation 8 Applying Total Quality 17 Research and Development Management 9 Training 18 Market Conditions/Customer Engagement RESEARCH OBJECTIVES AND METHODOLOGY The primary objectives of the present study can be summarized as follows: 1. Determine critical success factors for construction organizations. 2. Develop an organizational performance prediction model based on the determined critical success factors. To achieve the previous objectives, data regarding CSFs and organization performance are collected from various construction companies. The collected data is then classified and normalized to be ready for input to Artificial Neural Networks (ANNs). ANNs are used to test the significance of CSFs. After specifying the critical success factors, a NN organization performance model is developed and then validated. Sensitivity analysis is performed to show the effect of each critical success factor on organization performance.

355 NEURAL NETWORKS: MODELING TOOL Artificial Neural Networks (ANNs) attempt to model the brain learning, thinking, storage, and retrieval of information, as well as associative recognition. ANNs are introduced as hardware or software systems analogous to biological neural systems both in structure and in functionality (Moselhi et al. 1991). In the general form of a neural network, the unit analogous to biological neuron is referred to as processing element. A processing element (artificial neuron) performs summation ( ) and transfer function (F) to determine the value of its output. The network consists of many of those elements usually organized into a sequence of layers with full or partial connections between successive layers specifically designated (Moselhi et al. 1991). Figure 1 shows simple three-layer network architecture. Input Layer Hidden Layer Output Layer Input Pattern X 1 X 2 X n 1 2 n W ij F F F 1 2 m W jk F F 1 p Y 1 Y p Output Pattern Figure 1: Typical Example of Simple NN Architecture Similar to human thinking and decision making, an ANN takes previously trained problems to build a system of neurons that makes new decisions, classifications and forecasts. NNs learn patterns that are being presented during the training or learning phase. During the course of training, ANNs develop the ability to generalize, thereby becoming able to correctly classify new patterns or to make forecasts and predictions. Back Propagation Neural Networks (BPNNs) are one of the most common ANN structures, as they are simple and effective, and have found home in a wide assortment of machine learning applications. BPNNs start as a network of nodes arranged in three layers; the input, hidden, and output layers. The input and output layers serve as nodes to buffer input and output for the model, respectively, while the hidden layer serves to provide a means for input relations to be represented in the output. Before any data has been run through the network, the weights for the nodes are generated randomly. BPNNs support a contribution factor module which produces a number for each input parameter called a contribution factor that is a rough measure of the importance of that variable in predicting the network's output, relative to the other input parameters in the same network. The higher the number, the more the variable is contributing to the prediction or classification.

356 DATA COLLECTION After identifying the potential success factors that may affect the organization performance, a questionnaire was prepared to assess the effect of each factor on organization performance. Sixty three questionnaires were sent to several construction organizations to be filled across Canada (21), Egypt (31) and other countries (11). Decision makers in these organizations are invited to fill-in questionnaires reflecting their experience. A scale from 1 to 5 is used to to rate the effect of each factor on organization performance. In addition, a percentage is used to reflect the decision-maker openion regarding overall organization success. Sample of the raw data of two responses obtained from the questionnaire is given in Table 3. ORGANIZATION PERFORMANCE MODEL DEVELOPMENT There are several methods to assess the significance of independent factors affecting the performance of a dependent criterion. In the present research, ANNs and regression analysis are used to assess the most significant success factors and hence evaluate the performance for construction organizations. The collected data points-out different construction organizations worldwide that are used to extract an organization performance model based on the CSFs. Table 3. Sample of the Collected Raw Data Category Success Factor Responses (1~5) #1 #2 Clear Vision, Mission and Goals 5 5 Competition Strategy 3 5 Organizational Structure 5 5 Political Conditions 4 4 Number of Full Time Employees 5 5 Usage of International Aspects (ISO) 3 4 Availability of Knowledge 4 4 Usage of IT 5 5 Business Experience (no. of years) 4 4 Administrative and Legal Technical Management Market and Finance Product Maintenance 2 3 Employee Culture Environment 5 4 Employee Compensation and Motivation 5 4 Applying Total Quality Management 3 4 Training 3 4 Quick Liquid Assets 3 4 Feedback Evaluation 4 4 Research and Development 5 5 Market Conditions/Customer Engagement 5 5 Overall Company Performance (%) 70 80

357 Neural Network Training Before an ANN is trained, training criteria must be specified in advance. These include: the maximum and minimum absolute errors and number of training cycles without improvements. The data space is divided into three sets: training set, test set, and production set. The training set is used to train the network, where the error with respect to the training cycles will be calculated. The test set is used to test the network during development/training and to continuously correct it by adjusting the weights of network links in order to reduce the error. The production set is the part of data which is not shown and used to validate the model. Ranking of Input variables The purpose of establishing ranks of the input variables is to determine the relative importance of the variables and those mostly affect organizational performance. In this study, quantitative estimates of the contribution of all the 18 input (independent) variables are done. The contribution percentages are derived from an analysis of the weights of the trained neural network. The higher the number, the more that variable is contributing to the classification and/or prediction. Obviously, if a certain variable is highly correlated, the variable will have a high contribution percentage. In Table 4, the contribution percentage (relative significance) of the 18 success factors are given. The success factors are ranked according to their contribution percentages. The differences in contribution percentage between each two successive success factors are also given in Table 4. The difference in contribution percent between success factors No. 9 and 10 is 0.794 which is about seven times the immediate preceding difference in contribution percent (0.117). Therefore, only the first nine factors can be considered more significant (i.e. critical) factors. These nine critical success factors will be used to perform NN training and sensitivity analysis. Organization Performance Model The data of the critical success factors specified previously are used only again to train NN in order to obtain an organization performance model. The same training criteria are used (maximum and minimum absolute errors and number of training cycles without improvements). The data space is divided here into two sets: training set (80%, i.e. 48) and validation set (20%, i.e. 12). The training set is used to train the network, where the validation set is used to calculate validation parameters. After the NN is trained, it can be recalled to predict the organization performance for any given values of the critical success factors. CONCLUSIONS Construction is a highly competitive industry nowadays. Achieving success is based on many factors which have direct effect on the performance of construction organizations. In construction organizations, it is difficult to measure performance due to the diversity and complexity of construction organizations. Most of previous studies concentrated on determining critical success factors at the project level. In this paper, a regression model has been developed to predict construction organizations performance. A multi dimensional study on construction organizations performance has been conducted using 63 surveys obtained from various construction

358 organizations in Canada, Egypt, France, Greece, Germany, USA, Saudi Arabia and Dubai. The obtained data was analyzed using back propagation model of artificial neural networks, which was used to determine the relative significance of various success factors. After specifying the critical success factor, a NN organization performance model was developed. The model can be used to predict organization performance of construction companies based on estimated values of its CSFs. Table 4: Ranking and Relative Significance of Success Factors Rank Success Factor Contribution Difference in (%) Contribution (%) 1* Availability of Knowledge 7.13 --- 2* Clear Vision, Mission and Goals 7.02 0.114 3* Organizational Structures 6.73 0.282 4* Feedback Evaluations 6.63 0.101 5* Business Experience 6.55 0.082 6* Political Conditions 6.48 0.070 7* Research and Development 6.45 0.029 8* Employee Culture Environments 6.25 0.199 9* Competition Strategy 6.14 0.117 10 Market Conditions/Customer Engagement 5.34 0.794 11 Training 5.12 0.225 12 Number of Full Time Employees 5.01 0.105 13 Product Maintenance 4.82 0.193 14 Usage of IT 4.75 0.070 15 Quick Liquid Assets 4.37 0.378 16 Applying TQM 4.06 0.308 17 Usage of International Aspects (ISO) 3.99 0.070 18 Employee Compensation and Motivation 2.79 1.199 SUM 99.65 * Critical Success Factors REFERENCES: Abraham G. (2002). Identification of Critical Success Factors for Construction Organizations in the Architectural/Engineering/Construction (A/E/C) Industry, Ph.D. Thesis, Georgia Institute of Technology, Atlanta, GA, USA. Ahmad, I. and Dye, J. (1994). Development of a Database of MBE/DBE Firms and Decision Model to Determine their Capacity for the Florida Construction Industry, Technical Report No. 120, Department of Construction Management, Florida International University. Al-Barqawi, H. and Zayed, T. (2006) Condition Rating Model for Underground Infrastructure Sustainable Water Mains, J. of Performance of Constructed Facilities, ASCE, 20(2), 126-135. Aldrich, H. (1979). Organizations and Environments, Organization Studies, Englewood Cliffs, NEW Jersey, 9(1), 18-25.

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