The analytic hierarchy process to support decision-making processes in infrastructure projects with social impact

Abstract

There is an increasing awareness among all kinds of organisations (in business, government and civil society) about the benefits of jointly working with stakeholders to satisfy both their goals and the social demands placed upon them. This is particularly the case within corporate social responsibility (CSR) frameworks. In this regard, multi-criteria tools for decision-making like the analytic hierarchy process (AHP) described in the paper can be useful for the building relationships with stakeholders. Since these tools can reveal decision-maker's preferences, the integration of opinions from various stakeholders in the decision-making process may result in better and more innovative solutions with significant shared value. This paper is based on ongoing research to assess the feasibility of an AHP-based model to support CSR decisions in large infrastructure projects carried out by Red Eléctrica de España, the sole transmission agent and operator of the Spanish electricity system.

Relationship between Corporate Social Responsibility and Financial Performance in the Mineral Industry: Evidence from Chinese Mineral Firms

“…In recent years, studies also evaluated CSR performance based on the firms' CSR reports [44]. Furthermore, scholars even establish their own evaluation systems for specific industries such as the construction and infrastructure industry [45,46].…”

Application of analytical hierarchy process to support selection of difficult-to-quantify characteristics in new product development

“…The integration of opinions from various stakeholders, e.g. expert opinions, leads to better results of AHP (Á lvarez, Moreno, & Mataix, 2012). The evaluation of the criteria was derived from the 1000 point test by transforming the relative priorities of the test into the AHP scale.…”

Development and application of a value driven design assessment framework to an unmanned air system design

“…', assuming the existence of a ratio scale preference, rather than derive the preference through the use of a set of axioms, as done in Utility Theory, [131]. It is, however, a useful tool to define the problem, consider a large number of attributes, communicate value, identify differences and similarities between various stakeholders' points of view and aggregate them [133], [134], [135], [136], [137], [138]. Nevertheless, it is the synthesis of MAUT with AHP that provides significant benefits in the preferences' assessment, as suggested by Dyer [131], and has been successfully demonstrated in several applications, [139], [140], [141], [142], [143], [144], [145].…”

“…Hence, the design alternatives can be distinguished in terms of their cost related design attributes, using the minimum number of design variables for a fast design space exploration and optimization in the 135 conceptual design phase. These cost models are modular, allowing for easy integration, improvements/replacements in the later stages of design, and automation during the multidisciplinary design optimisation.…”

“…Quite often the preferential differences among different alternatives and priorities are also considered/weighted to obtain the group utility values, as in Huang [152]. AHP is used to support multiple stakeholders' decision making, Alvarez et al [135]; the geometric mean calculated by AHP is also used in group decision making Lai et al [134] and Sohn et al [141]; while fuzzy AHP techniques are employed in the geometric means computation by Carnero [153]. In an ordinal ranking and in the presence of strategic voting, the individuals' preferences can also be aggregated to an overall ranking as in Hurley et al [154].…”