Contractor Decision Research Articles

Compassionate Capitalism: Understanding the Juxtaposition of Personal Gain and Concern for Others

This article explores the concept of Compassionate Capitalism, which involves creating sustainable business models benefiting society and employees. Through Compassionate Capitalism, business owners can build trust with their customers and stakeholders, and in turn, enhance their long-term profitability through the Employee Stock Ownership Plan (ESOP) structure. The article also discusses the historical perspectives on Capitalism and articulates how Capitalism can coexist with compassion. It highlights the ESOP as a present-day example of Compassionate Capitalism, where employees are given a financial stake in the company for which they are employed. ESOPs redistribute ownership to the workforce and create a unique form of Capitalism that continues a successful company’s tradition. The ESOP benefits the younger members of a company, enhances retirement security, and creates a sense of ownership among employees. The article explains how employee ownership can align the interests of workers with those of management, creating a more collaborative, equitable workplace, contributing to greater overall economic growth. This article includes a case study examining a defense contractor's decision to become an employee-owned company through an ESOP transaction in 2010. The ESOP consulting firm, Executive Financial Services (EFS), provided a feasibility analysis and implementation for the transaction. Over the next decade, the company grew rapidly and eventually sold for $1.6 billion in 2020, creating hundreds of millionaires among the ESOP participants. This decision exemplifies the Compassionate Capitalism model, where the owners chose to honor and support the employees who helped build the company, creating generational wealth opportunities for myriad families. The decision to become an employee-owned company through an ESOP transaction shows that Capitalism and compassion can be compatible.

Green building incentive model during design recognition to ensure the reliability of green building operation and maintenance achievement

A low number in green building development cause the building construction will have an immense direct and indirect impact on future generations. This paper identified the optimum incentive model driving the building efficiency achievement in green building operation and maintenance. Critical factors that influence building consultant and contractor decision in green building design and construction toward the operation and maintenance achievement planned were analysed using partial least square structural equation modelling (PLS-SEM) methods. Precisely, the study modelled the regional policy affecting building efficiency achievement in the developing country. This research already identified the way of green building stakeholders’ operations and maintenance in the capital city of Indonesia. Quantitative method using case study analysis already validated for the questionnaire data. The PLS-SEM indicated that sustainable in green building implementation and sustainability has significant causal effects on the regional policy of building the life cycle. Among these factors, tax exemption, expedited permit, and floor to area ratio were significant. The regional policy indicators in green building implementation have the most significant effect on building efficiency achievement.

Understanding Green Construction Drivers for Incentive Structuring in the Developing Country

This paper is to identify the driving factors that influence building consultant and contractor decision in green construction properties and model the building incentive using partial least square structural equation modelling (PLS-SEM) methods. Precisely, the study modelled the effects of uncertainty of economy condition, environment development, knowledge improvement, and regional policy on green construction implementation affecting building incentives in the developing country. The study was based on a survey of building stakeholder in construction phase in the capital city of Indonesia and the model was validated for reliability and validity. The PLS-SEM indicated that sustainable in green construction implementation has significant causal effects on incentives. Among these factors, selection of green construction technology was significant. Sustainable in green construction implementation was, however, found to have the most significant effect on internal and external green construction incentives.

Multivariate discriminant analysis for assessing residential development projects from a contractor's perspective

The Korean construction market is constantly observing residential development projects performed together by developers and contractors. Although the development cost of the overall project is high, there are no standard methods to help contractors make decisions as to whether to participate in these residential development projects or not. This study proposes a simple and efficient model to assist contractors to determine successful projects with a more standardized and objective decision process. To achieve this goal, 31 project assessment items were selected and their weights were updated by AHP method with more sample sizes. Several independent samples t-tests were also carried out to identify the significant factors that affect the contractor's decision for residential development projects. Using multivariate discriminant analysis (MDA), a prediction model was developed with 109 residential development projects. The results indicated that 82% of the original grouped cases had been classified correctly. Cross validation showed that 81.5% of cross-validated grouped cases had been classified correctly. The simple and efficient model suggested in this study will help contractors make better decisions regarding whether to participate in the residential development projects or not.

Reliability Assessment of Research into Contractors’ Bidding Decisions

The most popular system for contracting construction works in the Polish market is the tender procedure. The selection of tenders in which a company will take part is very important as it plays a vital role in building its position on the market. Making a bid/no bid decision is influenced by a number of factors. The research carried out so far has been concerned mainly with identification of various kinds of factors influencing contractors’ bidding decisions. The present study describes a group of factors – identified using a survey method – that may influence the contractor's decision to participate in a tender procedure. An important problem revealed by the survey is assessment of experts’ opinions consistency. The method used in this paper for the consistency evaluation is Kendall's coefficient of concordance.

The neural network model for predicting the financing cost for construction projects

This study introduces the solution of a major problem facing contractors when submitting a tender for a new construction project. This problem is concerned with the estimation of the expected cost of finance and the maximum capital needs for new projects. In this study, the Artificial Neural Network (ANN) technique is used and proposed as a tool for management to better facilitate the prediction of the financing cost and the maximum capital needs for a new construction project. This prediction is valuable particularly at the tendering stage, when little information about the project cash flow and time schedule are known. The predictions of the two values have a great impact on the contractor's profit and affect the contractor's decision to tender. The inaccurate estimation of these two values could lead the project to stop without completion and may lead the contractor to insolvency.

A logistic regression approach to modelling the contractor's decision to bid

Significant factors in the decision to bid process are identified and a pro‐forma to elicit a numerical assessment of these factors is developed and validated using the bid/no‐bid decision‐makers from a UK construction company. Using the pro‐forma, data were collected from the collaborating company for historical bid opportunities. Statistical techniques are used to gain a better understanding of the data characteristics and to model the process. Eight variables have a significant relationship with the decision to bid outcome and for which the decision‐makers are able to discriminate. Factor analysis is used to identify the underlying dimensions of the pro‐forma and to validate functional decomposition of the factors. Finally, two logistic regression models of the decision to bid process are developed. While one model is ultimately rejected, the selected model is capable of classifying the total sample with an overall predictive accuracy rate of 94.8%. The results, therefore, demonstrate that the model functions effectively in predicting the bid/no‐bid decision process.

Factors considered in tendering decisions by top UK contractors

Decision-making involves a process by which a choice is selected from a number of options. Bid decisions by contractors are complex due to uncertainty about many factors affecting their outcomes. This study was able to identify, through a questionnaire survey, 55 factors characterizing the bid decision-making process. The questionnaire was mailed in August 1990, to 300 top contractors in the UK. The results indicate that several factors are considered equally important for bid/no bid and markup size decisions. Other factors are seen to have considerable importance for one decision but not for another. The need for work, the number of competitors tendering, and the amount of experience on such projects are identified as the top three factors that affect a contractor's decision to bid for a project. The degree of difficulty, the risk involving owing to the nature of the work, and the current work load are the highest ranked factors affecting markup size decisions.

An examination of the importance of resource considerations when contractors make project selection decisions

This research investigates the factors which building contractors consider when making project selection decisions and the processes they follow when making such decisions. The research is based on the premise that resource consideration is the most important factor for contractors when making project selection decisions. Literature was used to identify those factors thought to be most influential. The extent of the diversity of opinion disclosed indicated the necessity to obtain further information which was gathered from contractors by use of postal questionnaires and interviews. The information was analysed by ranking techniques and the results compared by Spearman rank correlations coefficient. The results obtained indicated the strengths of the factors identified. Spearman rank correlation coefficient showed a positive correlation for the results obtained from the findings of the literature study and those obtained from the survey. A non-weighted model of contractors' tendering decision process was developed. The model shows the inter-relationship that exists between client and contractor decisions. The model also illustrates the effect of such decisions on the actions of both clients (their representatives) and contractors. The results obtained from the survey shows that, ‘the ability of the client to pay for the cost of work’ is the most important factor contractors consider when making project selection decisions.

Competitive Bidding and Opportunity Costs

Methods of analyzing competitive bidding have treated construction projects as isolated elements to be optimized without regard to availability of work or other opportunity costs. This paper shows how resource restraints and opportunities can be easily included in these competitive bid analyses, so that a contractor's decisions concerning the overall firm or its individual projects reflect a broad view of the firm's market position. It is shown how markup decisions and predictions of corresponding expected values can take into consideration other possible projects and uses for the firm's limited resources, and also it can take into consideration estimated project costs that differ from the historical base. Consideration of opportunity costs and resource restrictions integrates data and decisions for greater understanding of a firm's competitive position and better business decisions in general. An example demonstrates the methods.