The futures of construction: A critical review of construction future studies

Anticipating the future is increasingly being seen as a useful way to align, direct and improve current organizational strategy. Several such 'future studies' have been produced which envision various construction industry scenarios which result from technological and socio-economic trends and influences. Thirteen construction-related future studies are critically reviewed. Most studies fail to address the complexities and uncertainties of both the present and the future, and fail to explore the connections between global, local, construction-specific and more widespread factors. The methodological approaches used in these studies do not generate any significantly different advice or recommendations for the industry than those emerging from the much larger canon of non-future oriented construction research. As such, these reports are less about the future than the present. If future studies are to make a worthwhile contribution to construction, it is critical that they develop our appreciation of the practical ability of stakeholders to influence some aspects of the future and not others, and an awareness of the competing agendas and the relative benefits and disadvantages of specific futures within the construction sector. Only then can future studies provide insights and help in preparing for the opportunities and threats the future may bring.

An entropy-based financial decision support system (e-FDSS) for project analysis in construction SMEs

Uncertainty contributes a major part in the accuracy of a decision-making process while its inconsistency is always difficult to be solved by existing decision-making tools. Entropy has been proved to be useful to evaluate the inconsistency of uncertainty among different respondents. The study demonstrates an entropy-based financial decision support system called e-FDSS. This integrated system provides decision support to evaluate attributes (funding options and multiple risks) available in projects. Fuzzy logic theory is included in the system to deal with the qualitative aspect of these options and risks. An adaptive genetic algorithm (AGA) is also employed to solve the decision algorithm in the system in order to provide optimal and consistent rates to these attributes. Seven simplified and parallel projects from a Hong Kong construction small and medium enterprise (SME) were assessed to evaluate the system. The result shows that the system calculates risk adjusted discount rates (RADR) of projects in an objective way. These rates discount project cash flow impartially. Inconsistency of uncertainty is also successfully evaluated by the use of the entropy method. Finally, the system identifies the favourable funding options that are managed by a scheme called SME Loan Guarantee Scheme (SGS). Based on these results, resource allocation could then be optimized and the best time to start a new project could also be identified throughout the overall project life cycle.

Training to address attitudes and behaviour of rural bank managers in Madhya Pradesh, India: a programme to facilitate financial inclusion

Given their physical presence in India, banks are arguably well-placed to improve financial inclusion in rural areas. However, uncertain repayment capacities and high transaction costs mean formal financial institutions are often reluctant to lend to the rural poor. Conversely, high transaction costs in dealing with banks are also incurred by clients, through, for example, lengthy, cumbersome and potentially ignominious procedures. Negative attitudes towards poor clients can be an important component of such transaction costs. An applied research project funded by the Enterprise Development Innovation Fund (EDIF-DFID) developed an innovative training programme designed to encourage more positive attitudes of bank staff towards poor clients, and towards their own role in rural poverty alleviation and development. This paper examines the development of the training programme, its implementation, and the results of its evaluation. It is shown that training can bring about attitudinal change, which in turn is reflected in behaviour and social impact. Copyright © 2007 John Wiley & Sons, Ltd.

Attitudes of rural branch managers in Madhya Pradesh, India, toward their role as providers of financial services to the poor

Discussions on banking reforms to reduce financial exclusion have referred little to possible attitudinal constraints, on the part of staff at both branch and institutional levels, inhibiting the provision of financial services to the poor. The research project, funded by the ESCOR (now Social Science Research) Small Grants Committee, has focused on this aspect of financial exclusion. The research commenced in May 2001 and was completed in April 2002. Profiles of the rural bank branch managers, including personal background, professional background and workplace, are presented. Attitudes of managers toward aspects of their work environment and the rural poor are examined, using results from both quantitative and qualitative analysis. Finally, the emerging policy implications are discussed. These include bank reforms to address human resource management, the work environment, intermediate bank management and organization, and the client interface.

Neural and wavelet network models for financial distress classification

This work analyzes the use of linear discriminant models, multi-layer perceptron neural networks and wavelet networks for corporate financial distress prediction. Although simple and easy to interpret, linear models require statistical assumptions that may be unrealistic. Neural networks are able to discriminate patterns that are not linearly separable, but the large number of parameters involved in a neural model often causes generalization problems. Wavelet networks are classification models that implement nonlinear discriminant surfaces as the superposition of dilated and translated versions of a single "mother wavelet" function. In this paper, an algorithm is proposed to select dilation and translation parameters that yield a wavelet network classifier with good parsimony characteristics. The models are compared in a case study involving failed and continuing British firms in the period 1997-2000. Problems associated with over-parameterized neural networks are illustrated and the Optimal Brain Damage pruning technique is employed to obtain a parsimonious neural model. The results, supported by a re-sampling study, show that both neural and wavelet networks may be a valid alternative to classical linear discriminant models.