Assessing impacts and community preparedness to cyclones: a fuzzy cognitive mapping approach

Caladcad

Journal of Open Innovation: Technology, Market, and Complexity

et al. 2019

The University technology transfer (UTT) process is hindered by various barriers to achieving a successful translation of innovative technologies from universities to industries and other partners. Identifying these various barriers and understanding their interrelationships would provide a better understanding of the complex nature of the UTT process, which may be considered as inputs to crucial decision-making initiatives. This paper addresses this gap by holistically determining UTT barriers and their intertwined relationships. Using the Delphi method and fuzzy cognitive mapping, a case study in a state university in the Philippines was conducted to carry out this objective. The Delphi process extracts 24 relevant barriers of UTT, out of 46 barriers obtained from a comprehensive review of the extant literature. The results show that misalignment between research and commercialization objectives is the barrier that was influenced most by the other barriers. In contrast, high costs of managing joint research projects in terms of time and money and institutional bureaucracy have the highest out-degree measures or are the barriers that influence other barriers the most. These findings provide guidelines to various stakeholders and decision-makers in understanding the existence of barriers in the formulation of strategies and initiatives for a successful UTT process.

Section: Fuzzy Cognitive Mappingmentioning

confidence: 99%

Open Innovation with Fuzzy Cognitive Mapping for Modeling the Barriers of University Technology Transfer: A Philippine Scenario

Caladcad

Journal of Open Innovation: Technology, Market, and Complexity

et al. 2019

“…We conducted the study with the aid of the FCM-based approach introduced by Kosko in 1986 [58]. The process of data capture in the FCM approach is considered quasi-quantitative because the quantification of concepts and links may be interpreted in relative terms [59] allowing participants to debate the cause-effect relations between the qualitative concepts while generating quantitative data based on their experiences, knowledge, and perceptions of inter-relationships between concepts [60][61][62][63][64][65][66][67][68]. The FCM approach helps us visualise how interconnected factors/ variables/ concepts affect one another while representing selfloop and feedback within complex systems [62,63,69].…”

Section: Methodsmentioning

confidence: 99%

“…The process of data capture in the FCM approach is considered quasi-quantitative because the quantification of concepts and links may be interpreted in relative terms [59] allowing participants to debate the cause-effect relations between the qualitative concepts while generating quantitative data based on their experiences, knowledge, and perceptions of inter-relationships between concepts [60][61][62][63][64][65][66][67][68]. The FCM approach helps us visualise how interconnected factors/ variables/ concepts affect one another while representing selfloop and feedback within complex systems [62,63,69]. A cognitive map is a signed digraph with a series of feedback comprising concepts (nodes) that describe system behavior and links (edges) representing causal relationships between concepts [60][61][62][63]65,[70][71][72].…”

Section: Methodsmentioning

confidence: 99%

“…The FCM approach helps us visualise how interconnected factors/ variables/ concepts affect one another while representing selfloop and feedback within complex systems [62,63,69]. A cognitive map is a signed digraph with a series of feedback comprising concepts (nodes) that describe system behavior and links (edges) representing causal relationships between concepts [60][61][62][63]65,[70][71][72]. FCMs may be created by individuals as well as by groups [60,72,73].…”

Section: Methodsmentioning

confidence: 99%

“…Individual cognitive mapping and group meeting approaches have their advantages and drawbacks [72]. FCMs allow the analysis of non-linear systems with causal relations, while their recurrent neural network behaviour [69,70,74] help in modelling complex and hard-to-model systems [61][62][63]. The FCM approach also provides the means to build multiple scenarios through system-based modelling [60][61][62][63][64]69,74,75].…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating poverty alleviation strategies in a developing country

2020

Self Cite

A slew of participatory and community-demand-driven approaches have emerged in order to address the multi-dimensional nature of poverty in developing nations. The present study identifies critical factors responsible for poverty alleviation in India with the aid of fuzzy cognitive maps (FCMs) deployed for showcasing causal reasoning. It is through FCM-based simulations that the study evaluates the efficacy of existing poverty alleviation approaches, including community organisation based micro-financing, capability and social security, market-based and good governance. Our findings confirm, to some degree, the complementarity of various approaches to poverty alleviation that need to be implemented simultaneously for a comprehensive poverty alleviation drive. FCM-based simulations underscore the need for applying an integrated and multi-dimensional approach incorporating elements of various approaches for eradicating poverty, which happens to be a multi-dimensional phenomenon. Besides, the study offers policy implications for the design, management, and implementation of poverty eradication programmes. On the methodological front, the study enriches FCM literature in the areas of knowledge capture, sample adequacy, and robustness of the dynamic system model. OPEN ACCESSCitation: Singh PK, Chudasama H (2020) Evaluating poverty alleviation strategies in a developing country. PLoS ONE 15(1): e0227176.

Risk Hazard & Crisis Pub Pol

Factors Affecting Disaster Resilience in Oman: Integrating Stakeholder Analysis and Fuzzy Cognitive Mapping

Al‐Manji

Lovett

Mitchell

2020

Planning for community resilience to disasters is a process that involves co‐ordinated action within and between relevant organizations and stakeholders, with the goal of reducing disaster risk. The effectiveness of this process is influenced by a range of factors, both positively and negatively, that need to be identified and understood so as to develop organizational capacity to build community resilience to disaster. This study investigates disaster planning and management in Oman, a country facing significant natural hazards, and with a relatively new system of institutional disaster management. Fuzzy cognitive mapping integrated with stakeholder analysis is used to identify relevant factors and their inter‐relationships, and hence provides an improved understanding of disaster governance. Developing an improved understanding of the complexity of this institutional behavior allows identification of opportunities to build greater resilience to disaster through improved planning and emergency response. We make recommendations for improved disaster management in Oman relating to governance (including improved plan dissemination and closer working with community organizations), risk assessment, public education, built environment development, and financing for disaster resilience.