Application of the ANP to the prioritization of project stakeholders in the context of responsible research and innovation

[EN] This paper presents a methodology to assess the stakeholders¿ influence in a research project within the context of responsible research and innovation. The methodology is based on a combination of the multicriteria decision making technique analytic network process and the key areas of responsible research. The method allows ranking and ordering the project¿s stakeholders based on their influence upon its responsibility. The purpose of such an assessment is to help research teams to more efficiently devote their limited resources to stakeholder management. The procedure is applied to a case study of the Information and Communication Technology business sector. It is an ongoing project at an early phase of development. Influential stakeholders have been identified first, and have been further classified into groups based on their relative importance. The assessment of their influence has been based on up to 16 different criteria, mainly belonging to the framework of responsible research and innovation. In the case study, the most influential criterion was the Capability to promote public engagement, while Developers were found to be the stakeholders most contributing to the research project responsibility. However, as explained, this is a temporary situation, valid for the current project development situation. It may vary over time as criteria vary in weight and stakeholders vary in influence.The authors would like to thank to our anonymous referees for their constructive comments and suggestions that helped us to improve the quality of the paper. Also, to the “Bolívar Gana con Ciencia” program from the Gobernación de Bolívar (Colombia) for the financial support. For the same reason, the authors are grateful to the Spanish Agencia Estatal de Investigación for its support of the project Propuesta de Indicadores para Impulsar el Diseño de Una Política Orientada al Desarrollo de Investigación e Innovación Responsable en España (CSO2016-76828-R)Ligardo-Herrera, I.; Gómez-Navarro, T.; Gonzalez-Urango, H. (2018). Application of the ANP to the prioritization of project stakeholders in the context of responsible research and innovation. Central European Journal of Operations Research. 1-23. https://doi.org/10.1007/s10100-018-0573-4S123Akbari N, Irawan CA, Jones DF, Menachof D (2017) A multi-criteria port suitability assessment for developments in the offshore wind industry. Renew Energy 102:118–133. https://doi.org/10.1016/j.renene.2016.10.035Aragonés-Beltrán P, García-Melón M, Montesinos-Valera J (2017) How to assess stakeholders’ influence in project management? A proposal based on the analytic network process. Int J Proj Manag. https://doi.org/10.1016/j.ijproman.2017.01.001Barrios Ortiz MA, De Felice F, Negrete KP et al (2016) An AHP-topsis integrated model for selecting the most appropriate tomography equipment. Int J Inf Technol Decis Mak 15:861–885. https://doi.org/10.1142/S021962201640006XBhupendra KV, Sangle S (2017) What drives successful implementation of product stewardship strategy? The role of absorptive capability. Corp Soc Responsib Environ Manag 24:186–198. https://doi.org/10.1002/csr.1394Botero C, Pereira C, Tosic M, Manjarrez G (2015) Design of an index for monitoring the environmental quality of tourist beaches from a holistic approach. Ocean Coast Manag 108:65–73. https://doi.org/10.1016/j.ocecoaman.2014.07.017Brugha R (2000) Stakeholder analysis: a review. Health Policy Plan 15:239–246. https://doi.org/10.1093/heapol/15.3.239Burget M, Bardone E, Pedaste M (2017) Definitions and conceptual dimensions of responsible research and innovation: a literature review. Sci Eng Ethics. https://doi.org/10.1007/s11948-016-9782-1Caballero-Luque A, Aragonés-Beltrán P, García-Melón M, Dema-Pérez C (2010) Analysis of the alignment of Company goals to Web content using ANP. Int J Inf Technol Decis Mak 9:419–436. https://doi.org/10.1142/S0219622010003889Claudia K, Köppl A, Stagl S (2014) Towards an operational measurement of socio-ecological performance. Working Paper no 52Colin E, Ackermann F (1998) Making strategy: the journey of strategic management. SAGE Publications Ltd, LondonDahlsrud A (2006) How corporate social responsibility is defined: an analysis of 37 definitions. Corp Soc Responsib Environ Manag 13:1–13. https://doi.org/10.1002/csrde Jong IM, Kupper F, Broerse J (2016) Inclusive deliberation and action in emerging RRI practices: the case of neuroimaging in security management. J Responsib Innov 3:26–49. https://doi.org/10.1080/23299460.2015.1137752De Lopez T (2001) Stakeholder management for conservation projects: a case study of Ream National Park, Cambodia. J Environ Manag 28:47–60De Lotto R, Gazzola V, Gossenberg S et al (2016) Proposal to reduce natural risks: analytic network process to evaluate efficiency of city planning strategies. Springer, Cham, pp 650–664European Commission (2011) DG Research workshop on Responsible Research & Innovation in EuropeGeoghegan-Quinn M (2012) Responsible research and innovation. Europe’s ability to respond to societal challengesGörener A (2012) Comparing AHP and ANP: an application of strategic decisions making in a Manufacturing Company. Int J Bus Soc Sci 3:194–208Jaafari A, Najafi A, García-Melón M (2015) Decision-making for the selection of a best wood extraction method: an analytic network process approach. For Policy Econ 50:200–209. https://doi.org/10.1016/j.forpol.2014.09.010Koops BJ (2015) The concepts, approaches, and applications of responsible innovations: an introduction. In: Koops BJ, Oosterlaken I, Romijn H, Swierstra T, van den Hoven J (eds) Responsible innovation 2: concepts, approaches, and applications. Springer, Dordrecht, pp 1–15Ligardo-Herrera I, Gómez-Navarro T, Inigo EA, Blok V (2018) Addressing climate change in responsible research and innovation: recommendations for its operationalization. Sustainability 10:20. https://doi.org/10.3390/su10062012Lubberink R, Blok V, van Ophem J, Omta O (2017) Lessons for responsible innovation in the business context: a systematic literature review of responsible, social and sustainable innovation practices. Sustainability. https://doi.org/10.3390/su9050721Mitchell RK, Agle BR, Wood DJ (1997) Toward a theory of stakeholder identification and salience: defining the principle of who and what really. Acad Manag Rev 22:853–886. https://doi.org/10.5465/AMR.1997.9711022105Owen R, Bessant J, Heintz M (2013) Responsible innovation: managing the responsible emergence of science and innovation in society. Wiley, New YorkPeris J, García-Melón M, Gómez-Navarro T, Calabuig C (2013) Prioritizing local agenda 21 programmes using analytic network process: a Spanish case study. Sustain Dev 21:338–352. https://doi.org/10.1002/sd.514Ramzan N, Degenkolbe S, Witt W (2008) Evaluating and improving environmental performance of HC’s recovery system: a case study of distillation unit. Chem Eng J 140:201–213. https://doi.org/10.1016/j.cej.2007.09.042Rosso M, Bottero M, Pomarico S et al (2014) Integrating multicriteria evaluation and stakeholders analysis for assessing hydropower projects. Energy Policy 67:870–881. https://doi.org/10.1016/j.enpol.2013.12.007Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48:9–26. https://doi.org/10.1016/0377-2217(90)90057-ISaaty TL (1994) How to make a decision: the analytic hierarchy process. Interfaces (Providence) 24:19–43Saaty TL (2001) The analytic network process: decision making with dependence and feedback. RWS Publications, PittsburghSaaty TL (2005) Theory and applications of the analytic network process: decision making with benefits, opportunities, costs, and risks. The Analytic Hierarchy Process (AHP) and its generalization to dependence and feedback, the Analytic Network Process (ANP), are methods of relative measurement of tangibles and intangibles. Being able to derive such measurements is essential for making goSaaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1:83. https://doi.org/10.1504/IJSSCI.2008.017590Saaty TL, Peniwati K (2008) Group decision making : drawing out and reconciling differences. RWS Publications, PittsburghSangle S, Babu PR (2007) Evaluating sustainability practices in terms of stakeholders’ satisfaction. Int J Bus Gov Ethics 3:56. https://doi.org/10.1504/IJBGE.2007.011934Shiau TA, Chuen-Yu JK (2016) Developing an indicator system for measuring the social sustainability of offshore wind power farms. Sustainability. https://doi.org/10.3390/su8050470Šijanec M, Žarnić R, Šelih J (2009) Multicriterial sustainability assessment of residential buildings. Technol Econ Dev Econ 15:612–630. https://doi.org/10.3846/1392-8619.2009.15.612-630Sipahi S, Timor M (2010) The analytic hierarchy process and analytic network process: an overview of applications. Manag Decis 48:775–808. https://doi.org/10.1108/00251741011043920Sólnes J (2003) Environmental quality indexing of large industrial development alternatives using AHP. Environ Impact Assess Rev 23:283–303. https://doi.org/10.1016/S0195-9255(03)00004-0Stahl BC, Coeckelbergh M (2016) Ethics of healthcare robotics: towards responsible research and innovation. Rob Auton Syst 86:152–161. https://doi.org/10.1016/j.robot.2016.08.018Stilgoe J, Owen R, Macnaghten P (2013) Developing a framework for responsible innovation. Res Policy 42:1568–1580. https://doi.org/10.1016/j.respol.2013.05.008Strand R, Spaapen J, Bauer MW et al (2015) Indicators for promoting and monitoring responsible research and innovation report from the expert group on policy indicatorsVaidya OS, Kumar S (2006) Analytic hierarchy process: an overview of applications. Eur J Oper Res 169:1–29. https://doi.org/10.1016/j.ejor.2004.04.028van de Poel I, Asveld L, Flipse S et al (2017) Company strategies for responsible research and innovation (RRI): a conceptual model. Sustainability 9:2045. https://doi.org/10.3390/su9112045Von Schomberg R (2011) Prospects for technology assessment in a framework of responsible research and innovation. Tech abschätzen lehren Bild transdisziplinärer Methoden. https://doi.org/10.1007/978-3-531-93468-6_2Wu X, Cui P (2016) A study of the time-space evolution characteristics of urban-rural integration development in a mountainous area based on ESDA-GIS: the case of the Qinling-Daba mountains in China. Sustainability 8:1085. https://doi.org/10.3390/su8111085Yüksel I, Dagdeviren M (2007) Using the analytic network process (ANP) in a SWOT analysis—a case study for a textile firm. Inf Sci (NY) 177:3364–3382. https://doi.org/10.1016/j.ins.2007.01.00

Prioritizing stakeholders to boost collaborative R&I projects benefits: an analytic network process approach

A methodology was developed to prioritize stakeholders of a collaborative research and innovation (R&I) project in the circular bioeconomy area, towards enhancing its benefits from a multi-perspective point of view. The concept of R&I project benefits was broken down into criteria, evaluating different attributes related to the project outputs and outcomes, to the project management processes, and to the social, environmental and economic dimensions. The devised methodology was based on a combination of the analytic network process multicriteria decision making method and the key benefit categories from the P5 standard for sustainability in project management. The P5 standard has been shown to adequately frame the benefits to stakeholders of R&I projects in the topic of circular bioeconomy. Key benefits identified by the experts relate to the categories “society and costumers” and “consumption”. The following stakeholders should have priority in the development of the project stakeholder management plan: research team members, leaders at the consortium organizations, project management team members and environmental NGOs. Future research will include a longitudinal study of the perceived stakeholder and benefit categories priority over time.The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CIMO (UIDB/00690/2020 and UIDP/00690/2020), SusTEC (LA/P/0007/2021) and CEMMPRE (UIDB/00285/2020). This article is a result of the project “BacchusTech - Integrated Approach for the Valorisation of Winemaking Residues” (POCI-01-0247-FEDER-069583), supported by the Competitiveness and Internationalization Operational Programme (COMPETE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF)info:eu-repo/semantics/publishedVersio

Addressing Climate Change in Research and Innovation Projects. A Tool for Anticipatory Carbon Footprint Calculation

[ES] El calentamiento global, y el cambio climático (CC) que produce, es una de las amenazas más globales y urgentes de las que es responsable la humanidad. El desafío de mitigar y adaptarse a la CC, entre otros, es una responsabilidad que ha alcanzado a todas las disciplinas, incluyendo el proceso de investigación e innovación. Durante más de 10 años, y como una forma de abordar estos grandes desafíos de nuestro tiempo, con la intención de fomentar la investigación responsable, la Comisión Europea ha estado promoviendo una temática transversal llamada: "Investigación e innovación responsable (RRI, en sus siglas en inglés)". El objetivo es sacar a la luz los problemas relacionados con la investigación y la innovación, anticipar sus consecuencias y hacer participar a la sociedad en el debate sobre la forma en que la ciencia y la tecnología pueden contribuir a crear el tipo de mundo y de sociedad que deseamos para las generaciones futuras. Esta tesis surge como un puente entre el gran desafío que representa el CC y la demanda por parte de la sociedad de investigación e innovación responsable, abordada en el contexto de la RRI. Los financiadores e impulsores de la investigación y la sociedad en su conjunto esperan que los equipos de investigación e innovación proporcionen resultados socialmente deseables, éticamente aceptables y sostenibles. Por lo tanto, la pregunta general que se responde en esta tesis es: ¿cómo sabe un equipo de investigación, sin ser especialista en evaluación ambiental, si su investigación es responsable de emisiones contribuyentes al cambio climático, y cómo puede incluir medidas para reducir o compensar esas emisiones de gases de efecto invernadero (GEI)? Para responder a esta pregunta, la presente tesis doctoral inicia con la descripción de los principales fundamentos que son centrales en ella: CC y RRI. En lo que respecta al primer concepto, explicamos la importancia y los medios para calcular la contribución al CC, principalmente el enfoque de la Huella de Carbono. En lo que respecta al segundo concepto, se explica la alineación de esta tesis el área clave de la sostenibilidad ambiental de la RRI, sus marcos sustantivos y sus dimensiones de anticipación y reflexividad. Una vez establecidos estos dos fundamentos, el cambio climático se aborda en el contexto de la RRI, revisando la literatura sobre los proyectos y propuestas de la RRI, incluyendo la sostenibilidad ambiental, y el CC en particular. Como resultado, surgieron dos avenidas de investigación, que se desarrollan en las siguientes secciones. Una avenida sobre cómo evaluar la influencia de las partes interesadas en un proyecto de investigación en el contexto de la RRI, desarrollada en el capítulo 3, y una avenida sobre la necesidad de nuevas herramientas basadas en bases de datos de acceso abierto para ayudar a los profesionales a integrar la prevención del CC en sus actividades de I + D. El capítulo 4, presenta el diseño de una novedosa herramienta con un algoritmo didáctico para la medición anticipada de la huella de carbono en los proyectos de investigación e innovación. Esta herramienta permite a los investigadores que no tienen formación en evaluación del impacto ambiental estimar las emisiones de gases de efecto invernadero de sus proyectos de investigación e innovación en las primeras etapas, momento en el que la anticipación y la reflexividad son las dimensiones fundamentales de la RRI.[CA] L'escalfament global, i el canvi climàtic (CC) que produeix, és una de les amenaces més globals i urgents de les que és responsable la humanitat. El desafiament de mitigar i adaptar-se a la CC, entre d'altres, és una responsabilitat que ha arribat a totes les disciplines, incloent el procés de recerca i innovació. Durant més de 10 anys, i com una forma d'abordar aquests grans desafiaments del nostre temps, amb la intenció de fomentar la investigació responsable, la Comissió Europea ha estat promovent una temàtica transversal anomenada: "Recerca i innovació responsable (RRI, en seves sigles en anglès)". L'objectiu és treure a la llum els problemes relacionats amb la investigació i la innovació, anticipar les seves conseqüències i fer participar la societat en el debat sobre la forma en què la ciència i la tecnologia poden contribuir a crear el tipus de món i de societat que desitgem per a les generacions futures. Aquesta tesi sorgeix com un pont entre el gran desafiament que representa el CC i la demanda per part de la societat d'investigació i innovació responsable, abordada en el context de la RRI. Els finançadors i impulsors de la investigació i la societat en el seu conjunt esperen que els equips de recerca i innovació proporcionin resultats socialment desitjables, èticament acceptables i sostenibles. Per tant, la pregunta general que respon a aquesta tesi és: com sap un equip d'investigació, sense ser especialista en avaluació ambiental, si la seva investigació és responsable d'emissions contribuents a el canvi climàtic, i com pot incloure mesures per reduir o compensar aquestes emissions de gasos d'efecte hivernacle (GEH)? Per respondre a aquesta pregunta, la present tesi doctoral s'inicia amb la descripció dels principals fonaments que són centrals en ella CC i RRI. Pel que fa a el primer concepte, expliquem la importància i els mitjans per calcular la contribució a l'CC, principalment l'enfocament de la Petjada de Carboni. Pel que fa a el segon concepte, s'explica l'alineació d'aquesta tesi l'àrea clau de la sostenibilitat ambiental de la RRI, els seus marcs substantius i les seves dimensions d'anticipació i reflexivitat. Un cop establerts aquests dos fonaments, el canvi climàtic s'aborda en el context de la RRI, revisant la literatura sobre els projectes i propostes de la RRI, incloent la sostenibilitat ambiental, i el CC en particular. Com a resultat, van sorgir dues avingudes de recerca, que es desenvolupen en les següents seccions. Una avinguda sobre com avaluar la influència de les parts interessades en un projecte d'investigació en el context de la RRI, desenvolupada en el capítol 3, i una avinguda sobre la necessitat de noves eines basades en bases de dades d'accés obert per ajudar els professionals a integrar la prevenció de CC en les seves activitats d'R + d. El capítol 4, presenta el disseny d'una nova eina amb un algoritme didàctic per al mesurament anticipada de la petjada de carboni en els projectes de recerca i innovació. Aquesta eina permet als investigadors que no tenen formació en avaluació de l'impacte ambiental estimar les emissions de gasos d'efecte hivernacle dels seus projectes de recerca i innovació en les primeres etapes, moment en el qual l'anticipació i la reflexivitat són les dimensions fonamentals de la RRI.[EN] Global Warming, and the climate change (CC) it produces, is one of the most global and urgent threats humankinds is responsible for. The challenge of mitigating and adapting to CC, among others, is a responsibility that has reached all disciplines, including the research and innovation (R&I) process. For more than 10 years, and as a way to tackle these great challenges of our time, with the intention of fostering responsible research, the European Commission has been promoting a cross-cutting issue named: "Responsible Research and Innovation (RRI)". The aim is to bring the problems (such as research integrity, non-inclusion of stakeholders, application of ethical or sustainability principles, etc.,.) related to R&I to light, to anticipate the possible consequences of R&I process and outcomes, and to engage society in the discussion of how science and technology can help create the kind of world and society we want for generations to come. This thesis emerges as a bridge between the great challenge represented by CC and the demand for responsibility from R&I process and outcomes, addressed in the context of RRI. Research funders and society as a whole claim that R&I teams must provide socially desirable, ethically acceptable, and sustainable outcomes. Hence, the general question to be answered in this thesis is: how does a research team, while not being specialists, know if its research is responsible for relevant contributions to CC, and how can they include measures to reduce or compensate such contributions (Greenhouse Gas emissions, GHG)? To respond to this question, the present dissertation begins with the main foundations that are central to it: CC and RRI. As regards the former concept, we explain the importance and means to calculate the contribution of GHG to CC, mainly the carbon footprint approach. In addition, regarding the latter, how this thesis aligns with the key RRIs' area of environmental sustainability, its substantive frameworks and its anticipation and reflexivity dimensions. Once these two foundations are established, CC is addressed in the context of RRI, reviewing the literature on RRI projects and proposals, which include environmental sustainability, and CC in particular. As a result, two avenues of research arise, which are developed in the following sections. An avenue about how to assess the stakeholders' influence in a research project within the context of RRI, which is developed in chapter 3, and an avenue about the need for new tools based on open-access databases to help practitioners to integrate CC prevention in their R&I activities. Chapter 4 presents the design of a novel tool with a didactic algorithm for anticipatory carbon footprint measuring in R&I projects. This tool allows researchers who are untrained in environmental impact assessment to estimate the greenhouse gas emissions of their R&I projects at early stages, when anticipation and reflexivity are the core RRI dimensions.Ligardo Herrera, IE. (2021). Addressing Climate Change in Research and Innovation Projects. A Tool for Anticipatory Carbon Footprint Calculation [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/165867TESI

A dynamic framework for managing the complexities of risks in megaprojects

The future of mega infrastructure projects is certain - there will be more risks to manage. The challenge is being met through research and innovation combining current approaches with new. This research adopted a dynamic approach through the combination of Analytical Network Process (ANP) and system dynamics (SD) as an innovative methodology known as SDANP to model complexity in megaprojects design and construction. We communicate how the SDANP model could explore problems caused by Social, Technical, Economic, Environmental and Political (STEEP) risks to construction cost, time and performance and provide insights that lead to organizational learning. We proceed to exemplify by means of a real-life case project in the City of Edinburgh and offer suggestions on what front-ended stakeholders could do to improve the management of risks in megaprojects. The results of the application showed that, when compared to traditional risks assessment methods, this SD model with integrated ANP revealed improvements in managing risks according to STEEP risks criteria. The new framework appears to be a superior solution for solving the dynamic complexities of risks during megaproject design and construction. The findings of the study contribute to the project management theoretical development within the field of megaproject management

Anticipating Environmental Burdens in Research and Innovation Projects. Application to the Case of Active and Healthy Ageing

[EN] In this paper; for research and innovation projects without environmental goals; a procedure is proposed to operationalize the anticipation and reflexivity of environmental concerns in the initial phases. By using the expert knowledge of specialists; we have first conducted a study to identify the general environmental topics relevant in any kind of research and innovation project not addressing the environment. In a second phase; a strategy is proposed to rank order the topics in terms of environmental relevance by means of the Analytic Hierarchy Process. To illustrate it; the case of Information and Communication Technologies for Active and Healthy Ageing is used because of its increasing importance; and because normal environmental targets are not considered. Results show that; in this case; the most relevant topic to be considered is the primary energy consumption by sources; followed by hazardous solid waste and consumption of non-renewable and scarce materials. According to the experts; these should be the main issues to be considered regarding the environmental sustainability of the outputs of such research and innovation projects. In conclusion; this paper contributes to a better understanding of how to promote a wider integration of environmental sustainability in research and innovation when environmental goals are not initially included.This work was funded by Spanish Agencia Estatal de Investigacion under grants [CSO2016-76828-R and BES-2017-081141]; and the Generalitat Valenciana under grant [AICO/2018/270].Monsonís-Payá, I.; Gómez-Navarro, T.; García-Melón, M. (2020). Anticipating Environmental Burdens in Research and Innovation Projects. Application to the Case of Active and Healthy Ageing. International Journal of Environmental research and Public Health (Online). 17(10):1-21. https://doi.org/10.3390/ijerph17103600S1211710Steffen, W., Grinevald, J., Crutzen, P., & McNeill, J. (2011). The Anthropocene: conceptual and historical perspectives. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 369(1938), 842-867. doi:10.1098/rsta.2010.0327Lewis, S. L., & Maslin, M. A. (2015). Defining the Anthropocene. Nature, 519(7542), 171-180. doi:10.1038/nature14258Geels, F. W. (2011). The multi-level perspective on sustainability transitions: Responses to seven criticisms. Environmental Innovation and Societal Transitions, 1(1), 24-40. doi:10.1016/j.eist.2011.02.002Wender, B. A., Foley, R. W., Hottle, T. A., Sadowski, J., Prado-Lopez, V., Eisenberg, D. A., … Seager, T. P. (2014). Anticipatory life-cycle assessment for responsible research and innovation. Journal of Responsible Innovation, 1(2), 200-207. doi:10.1080/23299460.2014.920121Funtowicz, S. O., & Ravetz, J. R. (1993). Science for the post-normal age. Futures, 25(7), 739-755. doi:10.1016/0016-3287(93)90022-lStilgoe, J., Owen, R., & Macnaghten, P. (2013). Developing a framework for responsible innovation. Research Policy, 42(9), 1568-1580. doi:10.1016/j.respol.2013.05.008Mejlgaard, N. (2017). Science’s disparate responsibilities: Patterns across European countries. Public Understanding of Science, 27(3), 262-275. doi:10.1177/0963662517724645RRI Tools RRI Tools: Towards RRI in actionwww.Rri-Tools.EuNordmann, A. (2013). (Im)Plausibility². International Journal of Foresight and Innovation Policy, 9(2/3/4), 125. doi:10.1504/ijfip.2013.058612Ligardo-Herrera, I., Gómez-Navarro, T., Inigo, E., & Blok, V. (2018). Addressing Climate Change in Responsible Research and Innovation: Recommendations for Its Operationalization. Sustainability, 10(6), 2012. doi:10.3390/su10062012Delvenne, P. (2017). Responsible research and innovation as a travesty of technology assessment? Journal of Responsible Innovation, 4(2), 278-288. doi:10.1080/23299460.2017.1328653Stemerding, D., Betten, W., Rerimassie, V., Robaey, Z., & Kupper, F. (2019). Future making and responsible governance of innovation in synthetic biology. Futures, 109, 213-226. doi:10.1016/j.futures.2018.11.005Bossink, B. (2018). The influence of knowledge flow on sustainable innovation in a project-based industry: From demonstration to limited adoption of eco-innovations. Journal of Cleaner Production, 193, 249-262. doi:10.1016/j.jclepro.2018.05.063De Medeiros, J. F., Ribeiro, J. L. D., & Cortimiglia, M. N. (2014). Success factors for environmentally sustainable product innovation: a systematic literature review. Journal of Cleaner Production, 65, 76-86. doi:10.1016/j.jclepro.2013.08.035Hemphill, T. A. (2016). Responsible innovation in industry: a cautionary note on corporate social responsibility. Journal of Responsible Innovation, 3(1), 81-87. doi:10.1080/23299460.2016.1178896Halme, M., & Korpela, M. (2013). Responsible Innovation Toward Sustainable Development in Small and Medium-Sized Enterprises: a Resource Perspective. Business Strategy and the Environment, 23(8), 547-566. doi:10.1002/bse.1801Silva, A. W. L. da, Selig, P. M., Lerípio, A. de Á., & Viegas, C. V. (2014). Strategic environmental assessment: one concept, multiple definitions. International Journal of Innovation and Sustainable Development, 8(1), 53. doi:10.1504/ijisd.2014.059222Motta, W. H., Issberner, L.-R., & Prado, P. (2018). Life cycle assessment and eco-innovations: What kind of convergence is possible? Journal of Cleaner Production, 187, 1103-1114. doi:10.1016/j.jclepro.2018.03.221Villares, M., Işıldar, A., van der Giesen, C., & Guinée, J. (2017). Does ex ante application enhance the usefulness of LCA? A case study on an emerging technology for metal recovery from e-waste. The International Journal of Life Cycle Assessment, 22(10), 1618-1633. doi:10.1007/s11367-017-1270-6Lobos, V., & Partidario, M. (2014). Theory versus practice in Strategic Environmental Assessment (SEA). Environmental Impact Assessment Review, 48, 34-46. doi:10.1016/j.eiar.2014.04.004Rivard, L., Lehoux, P., & Miller, F. A. (2019). Double burden or single duty to care? Health innovators’ perspectives on environmental considerations in health innovation design. BMJ Innovations, 6(1), 4-9. doi:10.1136/bmjinnov-2019-000348Chatfield, K., Iatridis, K., Stahl, B., & Paspallis, N. (2017). Innovating Responsibly in ICT for Ageing: Drivers, Obstacles and Implementation. Sustainability, 9(6), 971. doi:10.3390/su9060971Liotta, G., Ussai, S., Illario, M., O’Caoimh, R., Cano, A., Holland, C., … Palombi, L. (2018). Frailty as the Future Core Business of Public Health: Report of the Activities of the A3 Action Group of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA). International Journal of Environmental Research and Public Health, 15(12), 2843. doi:10.3390/ijerph15122843Smith, L., Tully, M., Jacob, L., Blackburn, N., Adlakha, D., Caserotti, P., … Koyanagi, A. (2020). The Association Between Sedentary Behavior and Sarcopenia Among Adults Aged ≥65 Years in Low- and Middle-Income Countries. International Journal of Environmental Research and Public Health, 17(5), 1708. doi:10.3390/ijerph17051708Porcelli, A. M., & Martínez, A. N. (2015). La nueva economía del siglo XXI: análisis de los impactos de la informática en el ambiente. Tendencias actuales en tecnologías informáticas verdes, un compromiso con la sustentabilidad. REVISTA QUAESTIO IURIS, 8(4). doi:10.12957/rqi.2015.20953Añón Higón, D., Gholami, R., & Shirazi, F. (2017). ICT and environmental sustainability: A global perspective. Telematics and Informatics, 34(4), 85-95. doi:10.1016/j.tele.2017.01.001Belkhir, L., & Elmeligi, A. (2018). Assessing ICT global emissions footprint: Trends to 2040 & recommendations. Journal of Cleaner Production, 177, 448-463. doi:10.1016/j.jclepro.2017.12.239Lubberink, R., Blok, V., van Ophem, J., & Omta, O. (2017). Lessons for Responsible Innovation in the Business Context: A Systematic Literature Review of Responsible, Social and Sustainable Innovation Practices. Sustainability, 9(5), 721. doi:10.3390/su9050721Hambling, T., Weinstein, P., & Slaney, D. (2011). A Review of Frameworks for Developing Environmental Health Indicators for Climate Change and Health. International Journal of Environmental Research and Public Health, 8(7), 2854-2875. doi:10.3390/ijerph8072854García-Melón, M., Pérez-Gladish, B., Gómez-Navarro, T., & Mendez-Rodriguez, P. (2016). Assessing mutual funds’ corporate social responsibility: a multistakeholder-AHP based methodology. Annals of Operations Research, 244(2), 475-503. doi:10.1007/s10479-016-2132-5Gómez-Navarro, T., García-Melón, M., Guijarro, F., & Preuss, M. (2017). Methodology to assess the market value of companies according to their financial and social responsibility aspects: An AHP approach. Journal of the Operational Research Society, 69(10), 1599-1608. doi:10.1057/s41274-017-0222-7Lee, C.-W., & Li, C. (2019). The Process of Constructing a Health Tourism Destination Index. International Journal of Environmental Research and Public Health, 16(22), 4579. doi:10.3390/ijerph16224579Zhang, S., Wei, Z., Liu, W., Yao, L., Suo, W., Xing, J., … Wang, J. (2015). Indicators for Environment Health Risk Assessment in the Jiangsu Province of China. International Journal of Environmental Research and Public Health, 12(9), 11012-11024. doi:10.3390/ijerph120911012Kazuva, E., Zhang, J., Tong, Z., Si, A., & Na, L. (2018). The DPSIR Model for Environmental Risk Assessment of Municipal Solid Waste in Dar es Salaam City, Tanzania. International Journal of Environmental Research and Public Health, 15(8), 1692. doi:10.3390/ijerph15081692Dos Santos, P. H., Neves, S. M., Sant’Anna, D. O., Oliveira, C. H. de, & Carvalho, H. D. (2019). The analytic hierarchy process supporting decision making for sustainable development: An overview of applications. Journal of Cleaner Production, 212, 119-138. doi:10.1016/j.jclepro.2018.11.270Mardani, A., Jusoh, A., MD Nor, K., Khalifah, Z., Zakwan, N., & Valipour, A. (2015). Multiple criteria decision-making techniques and their applications – a review of the literature from 2000 to 2014. Economic Research-Ekonomska Istraživanja, 28(1), 516-571. doi:10.1080/1331677x.2015.1075139Rametsteiner, E., Pülzl, H., Alkan-Olsson, J., & Frederiksen, P. (2011). Sustainability indicator development—Science or political negotiation? Ecological Indicators, 11(1), 61-70. doi:10.1016/j.ecolind.2009.06.009Saaty, T. L. (2004). Fundamentals of the analytic network process — Dependence and feedback in decision-making with a single network. Journal of Systems Science and Systems Engineering, 13(2), 129-157. doi:10.1007/s11518-006-0158-yFerwati, M. S., Al Saeed, M., Shafaghat, A., & Keyvanfar, A. (2019). Qatar Sustainability Assessment System (QSAS)-Neighborhood Development (ND) Assessment Model: Coupling green urban planning and green building design. Journal of Building Engineering, 22, 171-180. doi:10.1016/j.jobe.2018.12.006Alizadeh, M., Ngah, I., Hashim, M., Pradhan, B., & Pour, A. (2018). A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) Model for Urban Earthquake Vulnerability Assessment. Remote Sensing, 10(6), 975. doi:10.3390/rs10060975Huang, J.-Y., & Wey, W.-M. (2018). Application of Big Data and Analytic Network Process for the Adaptive Reuse Strategies of School Land. Social Indicators Research, 142(3), 1075-1102. doi:10.1007/s11205-018-1951-yGrošelj, P., Hodges, D. G., & Zadnik Stirn, L. (2016). Participatory and multi-criteria analysis for forest (ecosystem) management: A case study of Pohorje, Slovenia. Forest Policy and Economics, 71, 80-86. doi:10.1016/j.forpol.2015.05.006Xia, L., & Cheng, W. (2019). Sustainable development strategy of rural built-up landscapes in Northeast China based on ANP approach. Energy Procedia, 157, 844-850. doi:10.1016/j.egypro.2018.11.250Grošelj, P., & Zadnik Stirn, L. (2015). The environmental management problem of Pohorje, Slovenia: A new group approach within ANP – SWOT framework. Journal of Environmental Management, 161, 106-112. doi:10.1016/j.jenvman.2015.06.038Monsonís-Payá, I., García-Melón, M., & Lozano, J.-F. (2017). Indicators for Responsible Research and Innovation: A Methodological Proposal for Context-Based Weighting. Sustainability, 9(12), 2168. doi:10.3390/su9122168Res-AGorA Project Res-AGorAhttp://res-agora.eu/news/Pansera, M., & Owen, R. (2018). Innovation for de-growth: A case study of counter-hegemonic practices from Kerala, India. Journal of Cleaner Production, 197, 1872-1883. doi:10.1016/j.jclepro.2016.06.19

A dynamic framework for managing the complexities of risks in megaprojects.

The future of mega infrastructure projects is certain - there will be more risks to manage! The challenge is being met through research and innovation combining current approaches with new. This research adopted a dynamic approach through the combination of Analytical Network Process (ANP) and system dynamics (SD) as an innovative methodology known as SDANP to model complexity in megaprojects design and construction. We communicate how the SDANP model could explore problems caused by Social, Technical, Economic, Environmental and Political (STEEP) risks to construction cost, time and performance and provide insights that lead to organizational learning. We proceed to exemplify by means of a real-life case project in the City of Edinburgh and offer suggestions on what front-ended stakeholders could do to improve the management of risks in megaprojects. The results of the application showed that, when compared to traditional risks assessment methods, this SD model with integrated ANP revealed improvements in managing risks according to STEEP risks criteria. The new framework appears to be a superior solution for solving the dynamic complexities of risks during megaproject design and construction. The findings of the study contribute to the project management theoretical development within the field of megaproject management

A decision support model for identification and prioritization of key performance indicators in the logistics industry

YesPerformance measurement of logistics companies is based upon various performance indicators. Yet, in the logistics industry, there are several vaguenesses, such as deciding on key indicators and determining interrelationships between performance indicators. In order to resolve these vaguenesses, this paper first presents the stakeholder-informed Balanced Scorecard (BSC) model, by incorporating financial (e.g. cost) and non-financial (e.g. social media) performance indicators, with a comprehensive approach as a response to the major shortcomings of the generic BSC regarding the negligence of different stakeholders. Subsequently, since the indicators are not independent of each other, a robust multi-criteria decision making technique, the Analytic Network Process (ANP) method is implemented to analyze the interrelationships. The integration of these two techniques provides a novel way to evaluate logistics performance indicators from logisticians' perspective. This is a matter that has not been addressed in the logistics industry to date, and as such remains a gap that needs to be investigated. Therefore, the proposed model identifies key performance indicators as well as various stakeholders in the logistics industry, and analyzes the interrelationships among the indicators by using the ANP. Consequently, the results show that educated employee (15.61%) is the most important indicator for the competitiveness of logistics companies

Stakeholder engagement to evaluate tourist development plans with a sustainable approach

[EN] This study provides an evaluation of tourist development plans in the city of Cartagena de Indias (Colombia). Different stakeholders are involved in the search for solutions to this problem. The proposal is based on a model that combines two techniques, namely the analytic network process (ANP) and social network analysis (SNA). SNA is used to assess the relationships among stakeholders by identifying those who are most relevant and ANP is used to aggregate their opinions and evaluate tourist development plans of Cartagena to improve tourist experiences in a participatory way. The results suggest that the combination of SNA and ANP is a novel and suitable tool for strategic planning of a city.Bolivar Gana con CienciaGonzalez-Urango, H.; García-Melón, M. (2018). Stakeholder engagement to evaluate tourist development plans with a sustainable approach. Sustainable Development. 26(6):800-811. https://doi.org/10.1002/sd.1849S80081126

Designing walkable streets in congested touristic cities: the case of Cartagena de Indias, Colombia

This paper presents the case of Cartagena de Indias, a well-known international touristic destination in Colombia, which experiences serious problems of traffic congestion and accessibility to the city center. Promoting pedestrian mobility is one of the public administration's main goals, by enhancing and re-designing different pedestrian paths. Designing pedestrian zones is a context-specific multifaceted problem that involves multiple stakeholders and multiple criteria. A participatory multicriteria approach based on the Analytic Network Process (ANP) has been used to understand the most important characteristics affecting pedestrian mobility in Cartagena de Indias, thus deriving a useful decision-support tool for planning and designing pedestrian paths. In this respect, in this paper a set of streets in the city center has been evaluated, by combining the results of ANP with spatial data using Geographic Information Systems (GIS), producing thematic maps and an index of pedestrian priority to derive a priority of intervention. Some streets have been redesigned with the aim to increase their walking attractiveness. Results put the basis for discussion with local administration and stakeholders to validate them and propose further applications of the methodology

A Framework for Prioritizing Opportunities of Improvement in the Context of Business Excellence Model in Healthcare Organization

In today\u27s world, the healthcare sector is facing challenges to improve the efficiency and effectiveness of its operations. More and more improvement projects are being adopted to enhance healthcare services, making it more patient-centric, and enabling better cost control. Healthcare organizations strive to identify and carry out such improvement initiatives to sustain their businesses and gain competitive advantage. Seeking to reach a higher operational level of excellence, healthcare organizations utilize business excellence criteria to conduct assessment and identify organizational strengths and weaknesses. However, while such assessments routinely identify numerous areas for potential improvement, it is not feasible to conduct all improvement projects simultaneously due to limitations in time, capital, and personnel, as well as conflict with other organization\u27s projects or strategic objectives. An effective prioritization and selection approach is valuable in that it can assist the organization to optimize its available resources and outcomes. This study attempts to enable such an approach by developing a framework to prioritize improvement opportunities in healthcare in the context of the business excellence model through the integration of the Fuzzy Delphi Method and Fuzzy Interface System. To carry out the evaluation process, the framework consists of two phases. The first phase utilizes Fuzzy Delphi Method to identify the most significant factors that should be considered in healthcare for electing the improvement projects. The FDM is employed to handle the subjectivity of human assessment. The research identifies potential factors for evaluating projects, then utilizes FDM to capture expertise knowledge. The first round in FDM is intended to validate the identified list of factors from experts; which includes collecting additional factors from experts that the literature might have overlooked. When an acceptable level of consensus has been reached, a second round is conducted to obtain experts\u27 and other related stakeholders\u27 opinions on the appropriate weight of each factor\u27s importance. Finally, FDM analyses eliminate or retain the criteria to produce a final list of critical factors to select improvement projects. The second phase in the framework attempts to prioritize improvement initiatives using the Hierarchical Fuzzy Interface System. The Fuzzy Interface System combines the experts\u27 ratings for each improvement opportunity with respect to the factors deemed critical to compute the priority index. In the process of calculating the priority index, the framework allows the estimation of other intermediate indices including: social, financial impact, strategical, operational feasibility, and managerial indices. These indices bring an insight into the improvement opportunities with respect to each framework\u27s dimensions. The framework allows for a reduction of the bias in the assessment by developing a knowledge based on the perspectives of multiple experts