Simple Multi-Attribute Rating Research Articles

Multi-Criteria Relationship Analysis of Knowledge, Perception, and Attitude of Stakeholders for Engagement towards Maritime Pollution at Sea, Beach, and Coastal Environments

The external influence due to the severe repercussions of unprecedented and un-attended pollution has put vulnerable marine ecosystems at high risk of irreversible damage. This business-as-usual scenario could render them unfit to offer sustenance along with innumerable ecosystem services. Since the Stockholm Conference on Human Environment 1972, there have been global efforts to raise awareness, build capacity, and govern the pollution issue for a sustainable solution. However, there is a growing concern about the adequacy of the desired level of mobilization and readiness so far achieved at the level of various stakeholders to respond to the issue of maritime pollution. In this context, the present study was aimed at assessing the relationship between knowledge, perception, and attitude of the stakeholders regarding their engagement towards maritime pollution at sea, beach, and coastal environments, by incorporating multi-criteria quantitative analysis method for a case study of Karachi area in Pakistan. The structured questionnaire incorporates Knowledge, Perception, and Attitude as three key variables for three principles; four standardized criteria each, with eighteen indicators transformed into queries by applying MCDA’s Simple Multi Attribute Rating (SMART) technique for scoring to quantify the feedback of 304 respondents through a ratio scale having nominal qualifiable classes. The analysis has offered insight into the ways the stakeholders are perpetrating pollution and how their respective actions are important in the abatement of marine pollution. One-tailed Pearson Correlation analysis reveals insignificant relationship between the variables, indicating that the level of understanding of the stakeholders has not developed opinions to an extent that would enable an appropriate behavioral approach toward the abatement of the pollution. It suggests inducing awareness, mobilization, and reforms to encourage collective action by all actors.

Multi-Attribute Decision Making using Hybrid Approach based on Benefit-Cost Model for Sustainable Fashion

Multi-Attribute Decision Making (MADM) is used to select the best alternative from multi-alternatives based on multi-attribute (fashion material) and multi-criteria (sustainable fashion). Multi-alternatives are cotton, linen, silk, wool, acrylic, nylon, polyester, rayon, spandex, and mixed. Multi-attributes are material, texture, color, characteristic, comfort, and wearability. Multi-criteria are material fiber, smooth texture, faded color, elastic clothing, useful long, chilly and comfortable. Hybrid approaches and optimal solutions are needed to determine the best choice in decision making for both producers and consumers. The hybrid approach in MADM used is Simple Multi-Attribute Rating (SMART), Multi-Factor Evaluation Process (MFEP), Multi-Object Optimization based on Ratio Analysis (MOORA), Simple Additive Weighting (SAW), and Weighted Product (WP). SMART and MFEP are based on the Non-Benefit Cost Model while MOORA, SAW, and WP are based on a Benefit-Cost Model. The experimental results show that the SMART model with the best alternative is the rayon with the highest value (2.8333). The selection of the MFEP Model with the best alternative is rayon with the highest value (2.8330). The choice of MOORA model with the best alternative is rayon with the highest value (0.2595). The selection of the SAW Model with the best alternative is rayon with the highest value (0.8932). The selection of the WP Model with the best alternative is rayon with the highest value (0.1285). MADM using SMART, MFEP, MOORA, SAW, and WP for sustainable fashion yields the best alternative for consumption and production for the middle-class population in Indonesia.

Stochastic cognitive mapping to build common ground for selecting cases in research projects

Creating common ground among research groups is a prerequisite for scientifically sound case study research, especially in multi-national and multi-disciplinary research projects. Therefore, this paper proposes a new procedure for case study selection: stochastic cognitive mapping (sCM). sCM complements the previously illustrated conceptual content cognitive mapping (3CM) with email enquiry on concepts and their interconnections, simple multi-attribute rating and stochastic estimation of priorities. The procedure was applied to select case studies in a study on the role of community-based initiatives (CBIs) in societal change towards sustainability. The procedure performed well, based on project members’ evaluations, and enabled them to consistently identify a map and ranked list of criteria for selecting case initiatives. Researchers of the project had two to some extent exclusive orientations towards case selection: sampling and searching strategies, i.e. emphasis on the representativeness of a case set or on the features that interesting and useful study cases should possess. Strategies were integrated and CBIs selected through the sequence of snowball, random, and purposive samplings. Moreover, the method and the criteria list are adaptable to support comparable studies across diverse contexts.