Use Of Multi-criteria Decision Analysis Research Articles

Identification and mapping of groundwater recharge zones using multi influencing factor and analytical hierarchy process.

The management of groundwater systems is essential for nations that rely on groundwater as the principal source of communal water supply (e.g., Mohmand District of Pakistan). The work employed Remote Sensing and GIS datasets to ascertain the groundwater recharge zones (GWRZ) in the Mohmand District of Pakistan. Subsequently, a sensitivity analysis was conducted to examine the impact of geology and hydrologic factors on the variability of the GWRZ. The GWRZ was determined by employing weighted overlay analysis on thematic maps derived from datasets about drainage density, slope, geology, rainfall, lineament density, land use/land cover, and soil types. The use of multi-criteria decision analysis (MCDA) involves the utilization of the multi-influencing factor (MIF) and analytical hierarchy procedure (AHP) to allocate weights to the selected influencing factors. The MIF data found that very high groundwater recharge spanned 1.20%, high zones covered 40.44%, moderate zones covered 50.81%, and low zones covered 7.54%. In comparison, the AHP technique results suggest that 1.81% of the whole area is very high, 33.26 is high, 55.01% is moderate, and 9.92% has low groundwater potential. The geospatial-assisted multi-influencing factor approach helps increase conceptual knowledge of groundwater resources and evaluate possible groundwater zones.

Parallel multi-criteria decision analysis for sub-national prioritization of zoonoses and animal diseases in Africa: The case of Cameroon.

The use of multi-criteria decision analysis (MCDA) for disease prioritization at the sub-national level in sub-Sahara Africa (SSA) is rare. In this research, we contextualized MCDA for parallel prioritization of endemic zoonoses and animal diseases in The Adamawa and North regions of Cameroon. MCDA was associated to categorical principal component analysis (CATPCA), and two-step cluster analysis. Six and seven domains made of 17 and 19 criteria (out of 70) respectively were selected by CATPCA for the prioritization of zoonoses and animal diseases, respectively. The most influencing domains were "public health" for zoonoses and "control and prevention" for animal diseases. Twenty-seven zoonoses and 40 animal diseases were ranked and grouped in three clusters. Sensitivity analysis resulted in high correlation between complete models and reduced models showing the robustness of the simplification processes. The tool used in this study can be applied to prioritize endemic zoonoses and transboundary animal diseases in SSA at the sub-national level and upscaled at the national and regional levels. The relevance of MCDA is high because of its contextualization process and participatory nature enabling better operationalization of disease prioritization outcomes in the context of African countries or other low and middle-income countries.

Applying multi-criteria decision analysis in the bidding process of an own social security regime institute

Public administration is responsible for managing public resources and offering various types of services to the population. Therefore, to ensure that the maintenance of state bodies is carried out in a responsible manner, both for the acquisition of goods and for the contracting of services, it is necessary to carry out bidding processes that follow constitutional principles. In this way, the use of Multi-Criteria Decision Analysis (MCDA) methods can help decision makers to solve selection problems in public institutions. In this context, the objective of this paper is to select, from a pre-defined list of ten alternatives, the three cars considered most suitable to meet the needs of an Own Social Security Regime Institute, allowing the opening of a bidding process more reliable. To carry out this selection, three MCDA methods were used to aid the choice: Analytical Hierarchical Process (AHP), PROMETHEE II and Weighted Sum Method (WSM). The results demonstrated that the three methods applied together managed to select the three best cars for carrying out the bidding process and that the use of these tools has great potential for the area of public management.

Multi-criteria decision analysis: technique for order of preference by similarity to ideal solution for selecting greener analytical method in the determination of mifepristone in environmental water samples

This work proposes the use of multi-criteria decision analysis (MCDA) to select a more environmentally friendly analytical procedure. TOPSIS, which stands for Technique for Order of Preference by Similarity to Ideal Solution, is an example of a MCDA method that may be used to rank or select best alternative based on various criteria. Thirteen analytical procedures were used in this study as TOPSIS input choices for mifepristone determination in water samples. The input data, which consisted of these choices, was described using assessment criteria based on 12 principles of green analytical chemistry (GAC). Based on the objective mean weighting (MW), the weights for each criterion were assigned equally. The most preferred analytical method according to the ranking was solid phase extraction with micellar electrokinetic chromatography (SPE-MEKC), while solid phase extraction combined with ultra-high performance liquid chromatography tandem mass spectrometry (SPE-UHPLC-MS/MS) was ranked last. TOPSIS ranking results were also compared to the green metrics NEMI, Eco-Scale, GAPI, AGREE, and AGREEprep that were used to assess the greenness of thirteen analytical methods for mifepristone determination. The results demonstrated that only the AGREE metric tool correlated with TOPSIS; however, there was no correlation with other metric tools. The analysis results suggest that TOPSIS is a very useful tool for ranking or selecting the analytical procedure in terms of its greenness and that it can be easily integrated with other green metrics tools for method greenness assessment.

Towards circular economy indicators: Evidence from the European Union.

The European Union (EU) is moving towards sustainable development, and a key role is played by circular economy (CE) models geared towards reducing pressure on natural resources, generating jobs and fostering economic opportunities. Indicators are able to aggregate a variety of information and their use, through the use of multi-criteria decision analysis (MCDA), allows the performance of alternatives to be monitored. This work aims to calculate the performance of the EU27 in the years 2019 and 2020 according to 15 CE indicators available on Eurostat. The results of the Analytic Hierarchy process show that the greatest impact on circularity is determined by the category 'competitiveness and innovation', which together with the category 'global sustainability and resilience' accounts for two thirds of the overall weight. The MCDA results show that Belgium prevails in both the baseline and alternative scenarios, ahead of Italy and the Netherlands respectively. In general, circular policies see western European countries excel, while the performance of eastern European countries is weaker. The implications of this work highlight the three main barriers to the development of CE models: (i) illegal waste management; (ii) lack of knowledge and low level of investment in circular technologies and (iii) low distribution of value among stakeholders. In this way, resource management based on circularity will enable Europe to meet the challenges of sustainability with less dependence on imported raw materials.

Review and Bibliographic Analysis of Metaheuristic Methods in Multicriteria Decision‐Making: A 45‐Year Perspective Across International, Latin American, and Colombian Contexts

This study performs a systematic review and analysis of research on the use of multicriteria decision analysis (MCDA) methods for more than 45 years. More than 34,468 documents related to MCDA were identified in more than 130 countries, with an annual increase of 14.15% in scientific production. India, China, and Iran lead in number of publications, covering about 35%. The universities with the most publications include Islamic Azad University and Vilnius Gediminas Technical University. The main journals in this field are Expert Systems With Applications, Sustainability, and Journal of Cleaner Production. The most prolific authors are Zavadskas, E.; Wang, J.; Tzeng, GH; Zavadskas, EK; and Kahraman, C. The most popular methods are TOPSIS, AHP (analytic hierarchy process), VIKOR, PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation), and ANP. LATAN, which includes countries such as Brazil, Chile, and Mexico, has increased the use of MCDA in recent years. Colombia has also shown a great interest in MCDA, especially in the selection of alternative energy sources and industrial suppliers. This method of literature review provides a comprehensive overview of research at MCDA and offers valuable information for future projects and studies.

A rationing framework for Integrated Care Systems.

Background: Over the last 20 years, the NHS has introduced multiple changes in the commissioning structures, aiming at promoting integrated care in England. The Health and Care Bill 2021 is the latest and farthest-reaching reform, under which local commissioners from the emerging Integrated Care System (ICSs) are expected to accelerate the implementation of ‘integrated care programmes’. This overhaul demands local commissioners to develop approaches to robustly monitor and assess ‘integrated care programmes’ when making investment decisions. This research project aims to develop a framework to support the local commissioning of integrated care in England.
 Methods: To understand the decision-context and identify the most relevant monitoring and assessment criteria, we conducted 26 semi-structured interviews with local stakeholders. To supplement the criteria and inform the development of the evaluation component, we conducted a systematic literature review on the use of multi-criteria decision analysis (MCDA) in healthcare. The monitoring component of the framework is based on key performance indicators. A system-adjusted time-trend is used to identify ‘integrated care programmes’ that underperform and consequently should be re-assessed. For the evaluation component, we develop a value-measurement MCDA. To define the relative importance of the assessment criteria on the basis of public values, we conducted a discrete choice experiment (DCE) with members from the public in England. In the DCE, participants are presented with two hypothetical care programmes, described by six attributes (assessment criteria), and are asked to state their choice. The design of this experiment was informed by the literature, local stakeholders, and representatives from the Oxford & Thames Valley Patient and Public Involvement group. Standardised performance scores will be estimated using routinely collected data and quasi-experimental methods.
 Preliminary results: The emerging ICSs open an opportunity for local decision-makers to strengthen the commissioning process, and the proposed framework can potentially contribute to this end. In the interviews, stakeholders indicated that health outcomes, quality of care, cost and equity should be the main drivers of investment decisions. Similar criteria were used in 55 MCDA studies developed to guide priority-setting decisions in high-income countries, with most of these studies using the value-measurement approach. Intermediate health outcomes, compliance with national guidelines, quality of care and equity in access are defined as the monitoring criteria. The assessment criteria are six: final health outcomes, health-related quality of life, patient experience, size of the target population, equity and cost. According to the DCE conducted with 440 members from the public, the six attributes are statistically significant.
 Next steps: The structure of the framework has been presented to local stakeholders. Based on the data available and how ICSs are starting to operate, it seems that the framework could be applied to support the local commissioning of integrated care. We will demonstrate the framework’s applicability with an evaluation of integrated mental health services in Oxfordshire. We also aim to incorporate social care into the framework and, with this, identify potential data gaps. Afterwards, we will develop a user-friendly software to facilitate the use of the framework by other ICSs across England.

A critical review of multicriteria decision analysis practices in planning of urban green spaces and nature-based solutions

Abstract Green spaces and nature-based solutions (NBS) are increasingly considered by land-use planning policies to respond to the multiple challenges related to sustainable development. The multiple benefits brought by NBS make the use of multicriteria decision analysis (MCDA) essential to optimally balance their use. MCDA offers a catalog of methods allowing to structure problems with multiple objectives and to help adopt the optimal solution. However, NBS planning is a recent discipline and research is still ongoing to make this practice more common. We carried out a critical literature review on MCDA-NBS tools and practices, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method on the Web of Science database. We selected 124 papers on the subject between 2000 and 2022. We present a state-of-the-art MCDA approach for NBS and green space planning by looking at where these practices are applied, why and how this process is conducted, and who is involved in it. We found that studies are usually conducted in the global North on a single case study with the help of experts involved in the criteria weighting phase and the help of GIS MCDA tools often integrating a direct ranking method or the AHP method.

Combining behavioural TOPSIS and six multi-criteria weighting methods to rank biomass fuel pellets for energy use in Sweden

EU energy and climate policies continue to drive interest in biomass fuel pellets which can be produced from a wide variety of feedstock. The use of multi-criteria decision analysis (MCDA) to support feedstock selection has the potential for more transparent and better decision-making. This study applies the behavioural TOPSIS, a prominent MCDA technique, to rank pellets for energy use in Sweden produced from under-utilised forest and agricultural biomass. Seven criteria were used to assess and rank the biomass pellets. The alternatives include 88 types of pellets from 11 biomass materials. Possible attitudes of an expert towards the risk of losses (risk averse, risk neutral and risk-seeking) were combined with six sets of criteria weights obtained using six weighting methods – a total of 18 input settings (scenarios). Despite having different input settings, almost identical results were obtained in all scenarios, meaning that the rankings were stable and consistent. Across all 18 scenarios, pellets produced from a reference spruce/pine sawdust blend are ranked ahead of other pellet types. Pellets produced from Scots pine bark exhibited stable and consistent rankings across all scenarios; and thus this biomass is the second-best overall. The next best materials overall are poplar, reed canary grass and wheat straw, whereas torrefied pellets (torrefied beech, poplar and wheat straw) were ranked last in all scenarios. Combining behavioural TOPSIS and a variety of criteria-weighting methods is a meaningful way of improving decision-making with respect to producing a more valid and reliable ranking of biomass fuel pellets for energy use in Sweden.

Reconstructing the value puzzle in health technology assessment: a pragmatic review to determine which modelling methods can account for additional value elements.

Health technology assessment (HTA) has traditionally relied on cost-effectiveness analysis (CEA) as a cornerstone of evaluation of new therapies, assessing the clinical validity and utility, the efficacy, and the cost-effectiveness of new interventions. The current format of cost-effectiveness analysis, however, does not allow for inclusion of more holistic aspects of health and, therefore, value elements for new technologies such as the impact on patients and society beyond its pure clinical and economic value. This study aimed to review the recent modelling attempts to expand the traditional cost-effectiveness analysis approach by incorporating additional elements of value in health technology assessment. A pragmatic literature review was conducted for articles published between 2012 and 2022 reporting cost-effectiveness analysis including value aspects beyond the clinical and cost-effectiveness estimates; searches identified 13 articles that were eligible for inclusion. These expanded modelling approaches mainly focused on integrating the impact of societal values and health equity in cost-effectiveness analysis, both of which were championed as important aspects of health technology assessment that should be incorporated into future technology assessments. The reviewed cost-effectiveness analysis methods included modification of the current cost-effectiveness analysis methodology (distributional cost-effectiveness analysis, augmented cost-effectiveness analysis, extended cost-effectiveness analysis) or the use of multi-criteria decision analysis. Of these approaches, augmented cost-effectiveness analysis appears to have the most potential by expanding traditional aspects of value, as it uses techniques already familiar to health technology assessment agencies but also allows space for incorporation of qualitative aspects of a product's value. This review showcases that methods to unravel additional value elements for technology assessment exist, therefore, patient access to promising technologies can be improved by moving the discussion from "if" to "how" additional value elements can inform decision-making.

Optimal site selection for floating photovoltaic systems based on Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA): a case study

ABSTRACT Solar energy is growing rapidly thanks to reduced implementation costs; European development plans foresee a significant increase in installed capacity in the coming years. In this scenario, new identifiable areas on water surfaces are needed. This would lead to the development of floating photovoltaic (FPV) systems, which would mitigate the environmental impacts of terrestrial installations. Therefore, it is necessary to develop and propose a methodology to identify suitable sites for the installation of such plants. This study is useful to demonstrate the potential benefits that can be achieved using the methods of GIS for the optimal siting of FPVs in combination with the use of MCDA in areas such as Sicily, which are characterised by high summer temperatures. Seven watersheds were studied and the best site for FPV allocation selected using MCDA is San Giovanni Dam.

Application Framework of Multi-Criteria Methods in Sustainability Assessment

In the contemporary literature on sustainability, one can notice an increasingly frequent use of Multi-Criteria Decision Analysis (MCDA) methods instead of classic sustainability indices. The MCDA method should be tailored to the specific sustainability problem and decision situation so that its results are credible and satisfactory for the decision-maker. Therefore, the following research questions arise: (1) which MCDA methods are most often used in sustainability problems, and (2) which methods should be used depending on the characteristics of a particular sustainability decision problem and its assessment. The aim of the article is to scientifically analyse the applicability of various MCDA methods in decision-making problems related to sustainability, sustainable development, and sustainability assessment. In the article, based on the analysis of the literature, a set of features has been developed that determines the possibility of using individual MCDA methods in sustainability problems. Then, the characteristics of 28 methods are presented and the framework for selecting the MCDA method for the purpose of sustainability decision problems is indicated. As a result of the conducted research, it was found that the most commonly used MCDA methods in sustainability problems are primarily methods based on arithmetic aggregation of criteria. In addition, fuzzy methods and fuzzy modifications of classical methods are used more often. Research has established that MCDA methods are more functional than classic sustainability indices. In addition, the use of MCDA methods in the assessment of sustainability gives much more flexibility than the use of classic indices. The proposed framework allows the decision-maker to independently assess the potential of using individual multi-criteria methods in specific decision-making problems related to sustainability. The framework enables the selection of an appropriate MCDA method depending on the defined needs of the decision-maker, resulting from the decision problem, its structure, and decision-making situation.

SA1 The Use of Multi-Criteria Decision Analysis (MCDA) to Support Decision-Making in Healthcare: An Updated Systematic Literature Review

SA1 The Use of Multi-Criteria Decision Analysis (MCDA) to Support Decision-Making in Healthcare: An Updated Systematic Literature Review

An intelligent methodology for the use of multi-criteria decision analysis in impact assessment: the case of real-world offshore construction

Impact assessment of large-scale projects involves a plethora of technical, economic, social, and environmental factors that must be assessed along with the expectations of the stakeholders of each project. While impact assessment is required for a development project to receive regulatory approval to proceed, it is also an invaluable tool during the design phase of complex projects, providing for informed decision-making. Molding multiple perspectives of diverse stakeholders into a single collective choice is a key challenge in the process. Multi-Criteria Decision Analysis (MCDA) is the methodology used to rank a finite number of decision options based on a finite set of evaluation criteria. Different MCDA techniques, however, may lead to different decisions when applied to the same problem while different sets of criteria and weights may rank choices differently even when the same method is applied. This is a cause of concern, and even acrimony, amongst the stakeholders, often leading to protracted periods of negotiation and delaying project launching. The objective of this paper is to present an intelligent system to ameliorate the effects of the inherent subjectivity in MCDA techniques and to develop a consensus amongst the stakeholders in a data-driven setting. A case study from the field of offshore construction is used as a running example. This case study, informed by real-world experience in the field, demonstrates succinctly the issues involved and illustrates clearly the proposed intelligent methodology and its merits.

Use of Multi-Criteria Decision Analysis (MCDA) for Mapping Erosion Potential in Gulf of Mexico Watersheds

The evaluation of soil erosion is often assessed using traditional soil-loss models such as the Revised Universal Soil-Loss Equation (RUSLE) and the Soil and Water Assessment Tool (SWAT). These models provide quantitative outputs for sediment yield and are often integrated with geographic information systems (GIS). The work described here is focused on transitioning towards a qualitative assessment of erosion potential using Multi-Criteria Decision Analysis (MCDA), for improved decision-support and watershed-management prioritization in a northern Gulf of Mexico coastal watershed. The foundation of this work conceptually defined watershed erosion potential based on terrain slope, geomorphology, land cover, and soil erodibility (as defined by the soil K-factor) with precipitation as a driver. These criteria were evaluated using a weighted linear combination (WLC) model to map generalized erosion potential. The sensitivity of individual criteria was accessed with the one-at-a-time (OAT) method, which simply removed one criterion and re-evaluated erosion potential. The soil erodibility and slope were found to have the most influence on erosion-potential modeling. Expert input was added through MCDA using the Analytical Hierarchy Process (AHP). The AHP allows for experts to rank criteria, providing a quantitative metric (weight) for the qualitative data. The individual AHP weights were altered in one-percent increments to help identify areas of alignment or commonality in erosion potential across the drainage basin. These areas were used to identify outliers and to develop an analysis mask for watershed management area prioritization. A comparison of the WLC, AHP, ensembled model (average of WLC and AHP models), and SWAT output data resulted in visual geographic alignment between the WLC and AHP erosion-potential output with the SWAT sediment-yield output. These observations yielded similar results between the qualitative and quantitative erosion-potential assessment approaches, with alignment in the upper and lower ranks of the mapped erosion potentials and sediment yields. The MCDA, using the AHP and ensembled modeling for mapping watershed potential, provided the advantage of more quickly mapping erosion potential in coastal watersheds for improved management of the environmental resources linked to erosion.

Early Access to Medicines: Use of Multicriteria Decision Analysis (MCDA) as a Decision Tool in Catalonia (Spain).

Early access to medicines allows the prescription of a medicine before it is available in the public formulary to patients with severe or rare diseases with high unmet needs who have no authorised therapeutic alternatives available. In this context, consistent decision making is difficult, and a systematic assessment procedure could be useful to tackle complex situations and guarantee the equity of medicines’ access. A multidisciplinary panel (MP) conducted four workshops to develop an early access framework based on a reflective multiple criteria decision analysis (MCDA). A set of 12 criteria was agreed: eight quantitative (severity of disease, urgency, efficacy, safety, internal and external validity, therapeutic benefit and plausibility) and four qualitative (therapeutic alternative, existence of precedents, management impact and costs). Quantitative criteria were weighted using a five-point scale. The relative importance of quantitative criteria had mean weights from 4.7 to 3.6, showing its relevance in the decisions. The framework was tested using two case studies, and reliability was assessed by re-test. The re-test revealed no statistical differences, indicating the consistency and replicability of the framework developed. MCDA may help to structure discussions for heterogeneous treatment requests, providing predictability and robustness in decision making involving sensitive and complex situations.

Multi-Criteria Decision Analysis for Benefit-Risk Analysis by National Regulatory Authorities.

The approval process for pharmaceuticals has always included a consideration of the trade-offs between benefits and risks. Until recently, these trade-offs have been made in panel discussions without using a decision model to explicitly consider what these trade-offs might be. Recently, the EMA and the FDA have embraced Multi-Criteria Decision Analysis (MCDA) as a methodology for making approval decisions. MCDA offers an approach for improving the quality of these decisions and, in particular, by using quantitative and qualitative data in a structured decision model to make trade-offs in a logical, transparent and auditable way. This paper will review the recent use of MCDA by the FDA and EMA and recommend its wider adoption by other National Regulatory Authorities (NRAs) and the pharmaceutical industry.

Celebrating 10 years of the Journal of Comparative Effectiveness Research.

Celebrating 10 years of the Journal of Comparative Effectiveness Research.

Site Selection of Isolation Hospital for Coronavirus Patients in Nile Delta, Egypt, Using GIS Technology

Coronavirus disease (COVID‐19) is a viral infection caused by the SARS‐CoV‐2 virus that first surfaced in December 2019. According to the World Health Organization, most persons infected with this virus suffer from mild to severe respiratory infections and recover without specific treatment or hospitalization. Some people, however, may acquire serious illnesses that need medical attention and isolation facilities. This paper investigates the use of multi‐criteria decision analysis (MCDA) based on GIS technology to determine the optimal site selection for isolation hospitals for coronavirus patients in Nile Delta region in Egypt using the fuzzy analytical hierarchy process (F‐AHP) and the weighted overlay tool analysis method (WOA). The research of isolation hospital site selection in Nile Delta governorates in Egypt is one of the areas that have received insufficient attention due to the current global coronavirus epidemic. Several criteria are applied to identify and select the isolation hospital location, including World Health Organization regulations, Egyptian Ministry of Health conditions, previous research studies, and field visits. Geodatabase is created using ArcGIS Pro software, and manual digitization is done. As a conclusion of the study, numerous additional optimal sites for isolated hospitals have been found and chosen. There are around 29 proposed ideal sites for isolated hospitals utilizing F‐AHP and approximately 24 sites using WOA approach in Nile Delta region. These planned hospital locations might be permanent as a central hospital or temporary, to be relocated after the epidemic is over. The paper emphasizes the need to use the study criteria while selecting and defining the location of coronavirus isolation hospitals.

Modeling multi-criteria decision analysis in residential PV adoption

Multi-Criteria Decision Analysis (MCDA) is a sub-discipline of operations research that aims to solve multi-objective optimization problems by evaluating competing factors in decision-making. MCDA supports multidimensional decision-making processes through the analysis of diverse inputs at several levels of description, e.g. economic, technical, social, and environmental. The use of MCDA has been gaining momentum in the energy field, especially in endeavors that require evaluating the feasibility of different adoption scenarios for renewable energy technologies. The study presented in this paper investigates the combined use of multi-agent simulation, Bayesian modeling and sensitivity analysis for the development of a novel MCDA approach that supports the analysis of residential solar Photovoltaic (PV) adoption. The ensuing MCDA approach is evaluated alongside four popular MCDA methods (AHP, TOPSIS, SAW and ELECTREII) in a variety of tests aimed to assess overlap in criterion rankings and decision-making outcomes, covariation of criteria rankings in alternative scenarios, and the capacity to provide a model of correct decision-making. The comparative evaluation with AHP, TOPSIS, SAW and ELECTREII shows that overall ABM-BN-SA is well correlated with most of other MCDA methods and provides the best performing model of decision-making, with reference to the PV adoption use case under analysis. TOPSIS shows the closest fit with ABM-BN-SA, as expected since it used a ranking approach considerably closer to ABM-BN-SA as compared to the other MCDA treatments. ELECTREII yields the lowest degree of ranking overlap with ABM-BN-SA. All the reviewed methods have been illustrated and evaluated within our residential solar Photovoltaic (PV) adoption decision support system. In general, these methods enable a user to select an optimal solution out of a set of plausible alternatives according to multiple criteria and assist him in the design and exploration of the decision space. The ensuing decision-making methodology can be applied not only by Solar PV panel purchasers but also by stakeholders in other industries to logically and straightforwardly model and simulate the adoption decision-process of the public based on their individual preferences, behavioral rules, and interaction within a social network, with specific reference to a consumer utility function.