Competitiveness measurement system in the advertising sector

In this paper a new approach to find indicators that can be used to measure companies’ competitiveness and performance in an efficient and reliable way is presented. The aim is to assist managers of companies within a specific industrial sector by providing information about their relative position in the market so as to define better action plans that may improve the company’s performance. The approach combines the use of the Analytic Network Process, a multicriteria decision method, with the Balanced Scorecard. It allows the definition of a number of competitiveness indicators based on the performance and setting of the advertising sector. In this way it is possible to obtain a Competitiveness Index that allows a company to know its relative position with respect to other companies in the sector, and establish a ranking of the companies ordered by their competitiveness level. A case study in the advertising industry of Venezuela is provided. Results show that improvement plans for the agencies analyzed should promote creativity, innovation and the use of new technologies, as a particular form of innovation. These factors were considered to be the most relevant indicators in the advertising sector. The participating experts agreed that the methodology is useful and an improvement over current competitiveness assessment methods.Poveda Bautista, R.; García Melón, M.; Baptista, DC. (2013). Competitiveness measurement system in the advertising sector. SpringerPlus. 2(438):1-14. doi:10.1186/2193-1801-2-438S1142438Augusto M, Lisboa J, Yasin M, Figueira JR: Benchmarking in a multiple criteria performance context: An application and a conceptual framework. Eur J Oper Res 2008, 184(1):244-254. 10.1016/j.ejor.2006.10.052Barba-Romero S, Pomerol J: Decisiones Multicriterio. Fundamentos Teóricos y Utilización Práctica. Universidad de Alcalá, Madrid; 1997.Bell JA: Creativity, commercial popularity, and advertising expenditures. 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Process Mining Dashboard in Operating Rooms: Analysis of Staff Expectations with Analytic Hierarchy Process

[EN] The widespread adoption of real-time location systems is boosting the development of software applications to track persons and assets in hospitals. Among the vast amount of applications, real-time location systems in operating rooms have the advantage of grounding advanced data analysis techniques to improve surgical processes, such as process mining. However, such applications still find entrance barriers in the clinical context. In this paper, we aim to evaluate the preferred features of a process mining-based dashboard deployed in the operating rooms of a hospital equipped with a real-time location system. The dashboard allows to discover and enhance flows of patients based on the location data of patients undergoing an intervention. Analytic hierarchy process was applied to quantify the prioritization of the dashboard features (filtering data, enhancement, node selection, statistics, etc.), distinguishing the priorities that each of the different roles in the operating room service assigned to each feature. The staff in the operating rooms (n = 10) was classified into three groups: Technical, clinical, and managerial staff according to their responsibilities. Results showed different weights for the features in the process mining dashboard for each group, suggesting that a flexible process mining dashboard is needed to boost its potential in the management of clinical interventions in operating rooms. This paper is an extension of a communication presented in the Process-Oriented Data Science for Health Workshop in the Business Process Management Conference 2018.This project received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 812386.Martinez-Millana, A.; Lizondo, A.; Gatta, R.; Vera, S.; Traver Salcedo, V.; Fernández Llatas, C. (2019). Process Mining Dashboard in Operating Rooms: Analysis of Staff Expectations with Analytic Hierarchy Process. International Journal of Environmental research and Public Health. 16(2):1-14. https://doi.org/10.3390/ijerph16020199S114162Agnoletti, V., Buccioli, M., Padovani, E., Corso, R. M., Perger, P., Piraccini, E., … Gambale, G. (2013). Operating room data management: improving efficiency and safety in a surgical block. BMC Surgery, 13(1). doi:10.1186/1471-2482-13-7Marques, I., Captivo, M. E., & Vaz Pato, M. (2011). An integer programming approach to elective surgery scheduling. OR Spectrum, 34(2), 407-427. doi:10.1007/s00291-011-0279-7Haynes, A. B., Weiser, T. G., Berry, W. R., Lipsitz, S. R., Breizat, A.-H. S., Dellinger, E. P., … Gawande, A. A. (2009). A Surgical Safety Checklist to Reduce Morbidity and Mortality in a Global Population. New England Journal of Medicine, 360(5), 491-499. doi:10.1056/nejmsa0810119Dexter, F., Epstein, R. H., Traub, R. D., Xiao, Y., & Warltier, D. C. (2004). Making Management Decisions on the Day of Surgery Based on Operating Room Efficiency and Patient Waiting Times. Anesthesiology, 101(6), 1444-1453. doi:10.1097/00000542-200412000-00027Fernández-Llatas, C., Meneu, T., Traver, V., & Benedi, J.-M. (2013). Applying Evidence-Based Medicine in Telehealth: An Interactive Pattern Recognition Approximation. International Journal of Environmental Research and Public Health, 10(11), 5671-5682. doi:10.3390/ijerph10115671Westbrook, J. I., & Braithwaite, J. (2010). Will information and communication technology disrupt the health system and deliver on its promise? Medical Journal of Australia, 193(7), 399-400. doi:10.5694/j.1326-5377.2010.tb03968.xFisher, J. A., & Monahan, T. (2012). Evaluation of real-time location systems in their hospital contexts. International Journal of Medical Informatics, 81(10), 705-712. doi:10.1016/j.ijmedinf.2012.07.001Bath, P. A., Pendleton, N., Bracale, M., & Pecchia, L. (2011). Analytic Hierarchy Process (AHP) for Examining Healthcare Professionals’ Assessments of Risk Factors. Methods of Information in Medicine, 50(05), 435-444. doi:10.3414/me10-01-0028Lee, V. S., Kawamoto, K., Hess, R., Park, C., Young, J., Hunter, C., … Pendleton, R. C. (2016). Implementation of a Value-Driven Outcomes Program to Identify High Variability in Clinical Costs and Outcomes and Association With Reduced Cost and Improved Quality. JAMA, 316(10), 1061. doi:10.1001/jama.2016.12226Sloane, E. B., Liberatore, M. J., Nydick, R. L., Luo, W., & Chung, Q. B. (2003). Using the analytic hierarchy process as a clinical engineering tool to facilitate an iterative, multidisciplinary, microeconomic health technology assessment. Computers & Operations Research, 30(10), 1447-1465. doi:10.1016/s0305-0548(02)00187-9Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234-281. doi:10.1016/0022-2496(77)90033-5Bridges, J. F. P., Hauber, A. B., Marshall, D., Lloyd, A., Prosser, L. A., Regier, D. A., … Mauskopf, J. (2011). Conjoint Analysis Applications in Health—a Checklist: A Report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. Value in Health, 14(4), 403-413. doi:10.1016/j.jval.2010.11.013Proceedings of the 2011 annual conference on Human factors in computing systems - CHI ’11. (2011). doi:10.1145/1978942Anual Report 2014http://chguv.san.gva.es/documents/10184/81032/Informe_anual2014.pdf/713c6559-0e29-4838-967c-93380c24eff9Ratwani, R. M., Fairbanks, R. J., Hettinger, A. Z., & Benda, N. C. (2015). Electronic health record usability: analysis of the user-centered design processes of eleven electronic health record vendors. Journal of the American Medical Informatics Association, 22(6), 1179-1182. doi:10.1093/jamia/ocv050Van der Aalst, W. M. P., Reijers, H. A., Weijters, A. J. M. M., van Dongen, B. F., Alves de Medeiros, A. K., Song, M., & Verbeek, H. M. W. (2007). Business process mining: An industrial application. Information Systems, 32(5), 713-732. doi:10.1016/j.is.2006.05.00

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. 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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. 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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

Optimization of the supplier selection process in prefabrication using BIM

Prefabrication offers substantial benefits including reduction in construction waste, material waste, energy use, labor demands, and delivery time, and an improvement in project constructability and cost certainty. As the material cost accounts for nearly 70% of the total cost of the prefabrication project, to select a suitable material supplier plays an important role in such a project. The purpose of this study is to present a method for supporting supplier selection of a prefabrication project. The proposed method consists of three parts. First, a list of assessment criteria was established to evaluate the suitability of supplier alternatives. Second, Building Information Modelling (BIM) was adopted to provide sufficient information about the project requirements and suppliers’ profiles, which facilitates the storage and sharing of information. Finally, the Analytic Hierarchy Process (AHP) was used to rank the importance of the assessment criteria and obtain the score of supplier alternatives. The suppliers were ranked based on the total scores. To illustrate how to use the proposed method, it was applied to a real prefabrication project. The proposed method facilitates the supplier selection process by providing sufficient information in an effective way and by improving the understanding of the project requirements

An Analytic Hierarchy Process for The Evaluation of Transport Policies to Reduce Climate Change Impacts

Transport is the sector with the fastest growth of greenhouse gases emissions, both in developed and in developing countries, leading to adverse climate change impacts. As the experts disagree on the occurrence of these impacts, by applying the analytic hierarchy process (AHP), we have faced the question on how to form transport policies when the experts have different opinions and beliefs. The opinions of experts have been investigated by a means of a survey questionnaire. The results show that tax schemes aiming at promoting environmental-friendly transport mode are the best policy. This incentives public and environmental-friendly transport modes, such as car sharing and car pooling.Analytic Hierarchy Process, Transport Policies, Climate Change

Study on Performance Appraisal Method of Vocational Education Teachers using PROMETHEE II

Evaluating vocational education teachers’ performance is an important link of teaching management and an important guarantee of improving teaching quality. In conducting teaching, research and community service, vocational education teachers should weight more on quality than quantity. In this context, individual habit reacts to the demanded jobs which are influenced by his/her knowledge, attitude, and skill. Teacher’s performance evaluation is nothing but a Multi Criteria Decision Making Problem (MCDM). There are several quality attributes that influence the efficiency of a potential vocational education teacher while guiding his/her students towards a positive and value added academic outcome. However, the importance of quality attributes may differ from individuals’ perspective. In other words, different attributes may have different weightage according to their priority of significance while evaluating quality/performance level of a vocational education teacher. This paper makes the vocational education teachers’ performance appraisal quantitative and determines the evaluation index based on academic performance. Criteria for performance are: teaching load, publication, research, conferencing, consultancy, services, teaching attitude, teaching content, teaching method, and teaching effect. The Analytic Hierarchy Process (AHP) and PROMETHEE (Preference Ranking Organisation METHod for Enrichment Evaluations) II analysis were used in performance appraisal. Application feasibility of this method approach and guidelines in solving such a multi-attribute decision making problem has been described illustratively in this paper. It is also observed that this MCDM approach is a viable tool in solving the teacher selection decision problems. It allows the decision maker to rank the candidate alternatives more efficiently and easily. Keywords: performance, teaching, Analytic Hierarchy Process, PROMETHEE II