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Journal articles on the topic 'Cause-effect analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

Yakovleva, Anna, Sergyi Boichenko, and Angela Gay. "Cause-Effect Analysis of the Modern State in Production of Jet Fuels." Chemistry & Chemical Technology 8, no. 1 (March 15, 2014): 107–16. http://dx.doi.org/10.23939/chcht08.01.107.

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2

Biały, Witold, and Juraj Ružbarský. "Breakdown Cause and Effect Analysis. Case Study." Management Systems in Production Engineering 26, no. 2 (June 1, 2018): 83–87. http://dx.doi.org/10.1515/mspe-2018-0013.

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Abstract Every company must ensure that the production process proceeds without interferences. Within this article, the author uses the term “interferences” in reference to unplanned stoppages caused by breakdowns. Unfortunately, usually due to machine operators’ mistakes, machines break, which causes stoppages thus generating additional costs for the company. This article shows a cause and effect analysis of a breakdown in a production process. The FMEA as well as quality management tools: the Ishikawa diagram and Pareto chart were used for the analysis. Correction measures were presented which allowed for a significant reduction in the number of stoppages caused by breakdowns.

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3

Cucurachi, Stefano, and Sangwon Suh. "Cause-effect analysis for sustainable development policy." Environmental Reviews 25, no. 3 (September 2017): 358–79. http://dx.doi.org/10.1139/er-2016-0109.

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The sustainable development goals (SDGs) launched by the United Nations (UN) set a new direction for development covering the environmental, economic, and social pillars. Given the complex and interdependent nature of the socioeconomic and environmental systems, however, understanding the cause-effect relationships between policy actions and their outcomes on SDGs remains as a challenge. We provide a systematic review of cause-effect analysis literature in the context of quantitative sustainability assessment. The cause-effect analysis literature in both social and natural sciences has significantly gained its breadth and depth, and some of the pioneering applications have begun to address sustainability challenges. We focus on randomized experiment studies, natural experiments, observational studies, and time-series methods, and the applicability of these approaches to quantitative sustainability assessment with respect to the plausibility of the assumptions, limitations and the data requirements. Despite the promising developments, however, we find that quantifying the sustainability consequences of a policy action, and providing unequivocal policy recommendations based on it is still a challenge. We recognize some of the key data requirements and assumptions necessary to design formal experiments as the bottleneck for conducting scientifically defensible cause-effect analysis in the context of quantitative sustainability assessment. Our study calls for the need of multi-disciplinary effort to develop an operational framework for quantifying the sustainability consequences of policy actions. In the meantime, continued efforts need to be made to advance other modeling platforms such as mechanistic models and simulation tools. We highlighted the importance of understanding and properly communicating the uncertainties associated with such models, regular monitoring and feedback on the consequences of policy actions to the modelers and decision-makers, and the use of what-if scenarios in the absence of well-formulated cause-effect analysis.

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4

Varma, Jatin H., and Sachin Lal. "Root cause analysis of newsprint waste using pareto analysis and cause and effect matrix." IOP Conference Series: Materials Science and Engineering 810 (May 5, 2020): 012072. http://dx.doi.org/10.1088/1757-899x/810/1/012072.

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5

Onyusheva, Irina, Cherry Thinn Naing, and Aung Lin Zaw. "THE US-CHINA TRADE WAR: CAUSE-EFFECT ANALYSIS." EUrASEANs: journal on global socio-economic dynamics, no. 1(14) (January 30, 2019): 07–15. http://dx.doi.org/10.35678/2539-5645.1(14).2019.07-15.

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The US-China trade war has been considered one of the urgent issues in today’s international trade. In fact, this is a trade war where top two largest world economies participate. In this paper, we have made an attempt to describe and explain the motives behind this trade war along with the potential threats, causes and effects for the world economy. Comparative macroeconomic analysis of the involved countries has been also conducted. Possible retaliation of China and other countries to the US’ increasing in tariffs on imported goods, protectionism and deficit issues in the US are discussed in the first part of the paper. The authors focus on the potential threats and effects that this trade war may have on the world economy from the political economy point of view. Authors’ vision on the future prospects of the current situation is also presented.

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Sirotinskaya, Susanna V. "Cause-Effect Analysis in Assessment of Mineral Resources." Natural Resources Research 13, no. 1 (March 2004): 17–28. http://dx.doi.org/10.1023/b:narr.0000023304.47632.b9.

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7

Jiang, Bernard C., and Charles A. Gainer. "A cause-and-effect analysis of robot accidents." Journal of Occupational Accidents 9, no. 1 (June 1987): 27–45. http://dx.doi.org/10.1016/0376-6349(87)90023-x.

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8

Serebrennikov, Sergei Viktorovich. "Binary cause-and-effect model." Litera, no. 6 (June 2020): 75–81. http://dx.doi.org/10.25136/2409-8698.2020.6.33139.

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The subject of this research is the abstract model of functionality of cause-and-effect relations, which can be depicted as a construct x leads to y, where x is producing cause, and y – the effect. Leaning on semantics of the text, this model can be presented as P, cause that leads to P effect, where P is any semantic predicate, being on the place of either causing element or the effect element. The object of this research is the texts with cause-and-effect semantics. The goal consists in examination of the structure of cause-and-effect relations from the perspective of the logic of language and the semantic of text. The main conclusion is the depiction of cause-and-effect relations through the simplest binary model, in which the chain is created between to situations of propositions. Elements of the model are interconnected – one situation exists because of the other. Such relationship can be determined on the in-depth level – a semantic element equal for both situations, as well as on the meaningful level – connection between the elements of the situation. The study applies the methods of component analysis, propositional analysis, and contextual analysis. Relevance of the article is substantiated by referring to such modern linguistic trends as the logic of language, semantic syntax, and syntax of the text.

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9

Chowdhury, Dr Sanjoy, Dr Priyanka, Dr Snehakumari, and Drnilanjanchowdhury Drnilanjanchowdhury. "Analysis of Delayed Dislocation of IoL: Cause And Effect." IOSR Journal of Dental and Medical Sciences 16, no. 04 (April 2017): 01–05. http://dx.doi.org/10.9790/0853-1604090105.

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10

Blöbaum, Patrick, Dominik Janzing, Takashi Washio, Shohei Shimizu, and Bernhard Schölkopf. "Analysis of cause-effect inference by comparing regression errors." PeerJ Computer Science 5 (January 21, 2019): e169. http://dx.doi.org/10.7717/peerj-cs.169.

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We address the problem of inferring the causal direction between two variables by comparing the least-squares errors of the predictions in both possible directions. Under the assumption of an independence between the function relating cause and effect, the conditional noise distribution, and the distribution of the cause, we show that the errors are smaller in causal direction if both variables are equally scaled and the causal relation is close to deterministic. Based on this, we provide an easily applicable algorithm that only requires a regression in both possible causal directions and a comparison of the errors. The performance of the algorithm is compared with various related causal inference methods in different artificial and real-world data sets.

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11

Leng, David, and Nina F. Thornhill. "Process Disturbance Cause & Effect Analysis Using Bayesian Networks." IFAC-PapersOnLine 48, no. 21 (2015): 1457–64. http://dx.doi.org/10.1016/j.ifacol.2015.09.730.

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12

Kufelnicki, A., S. Lis, and G. Meinrath. "Application of cause-and-effect analysis to potentiometric titration." Analytical and Bioanalytical Chemistry 382, no. 7 (June 28, 2005): 1652–61. http://dx.doi.org/10.1007/s00216-005-3317-y.

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13

Gill, Avninder. "An effect‐cause‐effect analysis of project objectives and trade‐off assumptions." International Journal of Managing Projects in Business 1, no. 4 (September 12, 2008): 535–51. http://dx.doi.org/10.1108/17538370810906246.

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14

Dobrusskin, Christoph. "On the Identification of Contradictions Using Cause Effect Chain Analysis." Procedia CIRP 39 (2016): 221–24. http://dx.doi.org/10.1016/j.procir.2016.01.192.

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15

Hardenberg, Achaz von, and Alejandro Gonzalez-Voyer. "DISENTANGLING EVOLUTIONARY CAUSE-EFFECT RELATIONSHIPS WITH PHYLOGENETIC CONFIRMATORY PATH ANALYSIS." Evolution 67, no. 2 (September 24, 2012): 378–87. http://dx.doi.org/10.1111/j.1558-5646.2012.01790.x.

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16

Walter, S. D. "Cause-deleted proportional mortality analysis and the healthy worker effect." Statistics in Medicine 5, no. 1 (January 1986): 61–71. http://dx.doi.org/10.1002/sim.4780050109.

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17

Nakajo, T., and H. Kume. "A case history analysis of software error cause-effect relationships." IEEE Transactions on Software Engineering 17, no. 8 (1991): 830–38. http://dx.doi.org/10.1109/32.83917.

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18

Hu, Wenkai, Jiandong Wang, Tongwen Chen, and Sirish L. Shah. "Cause-effect analysis of industrial alarm variables using transfer entropies." Control Engineering Practice 64 (July 2017): 205–14. http://dx.doi.org/10.1016/j.conengprac.2017.04.012.

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19

Grote, Matthias, Werner Brack, Helge A. Walter, and Rolf Altenburger. "CONFIRMATION OF CAUSE–EFFECT RELATIONSHIPS USING EFFECT-DIRECTED ANALYSIS FOR COMPLEX ENVIRONMENTAL SAMPLES." Environmental Toxicology and Chemistry 24, no. 6 (2005): 1420. http://dx.doi.org/10.1897/04-278r.1.

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20

Kumar, Mukesh, Dixit Garg, and Ashish Agarwal. "An Analysis of Inventory Attributes in Leagile Supply Chain: Cause and Effect Analysis." International Journal of Mathematical, Engineering and Management Sciences 4, no. 4 (August 1, 2019): 870. http://dx.doi.org/10.33889/ijmems.2019.4.4-069.

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In the recent era, managing a supply chain efficiency is the necessity of any business due to shorter product life cycle and market penetration order uncertainty. With the increased competition globally, organizations need to be more efficient and responsive. This situation drives attention to the concept of Leagile Supply Chain (LASC). Therefore, more attention to Lean and agile inventory attributes are advocated as the foundation to sustain competitive LASC. Trade–off between the lean and agile supply chain inventory attributes and interrelationship. Upstream lean practise based on push system and downstream agile practices based on pull system. In this paper, the cause and effect analysis is to measure the influence of integrated LASC inventory attributes on the Supply Chain Performance (SCP). From the managerial viewpoint, cause and effect diagram provides the proactive understanding to positive and negative inventory attributes affects SCP.

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21

Vie, Gunnhild Åberge, Robyn E. Wootton, Johan Håkon Bjørngaard, Bjørn Olav Åsvold, Amy E. Taylor, Maiken Elvestad Gabrielsen, George Davey Smith, Pål Richard Romundstad, and Marcus R. Munafò. "The effect of smoking intensity on all-cause and cause-specific mortality—a Mendelian randomization analysis." International Journal of Epidemiology 48, no. 5 (May 10, 2019): 1438–46. http://dx.doi.org/10.1093/ije/dyz081.

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Abstract Background Smoking is an important cause of mortality and recent studies have suggested that even low-intensity smoking might be associated with increased mortality. Still, smoking is associated with lower socio-economic status as well as other potential risk factors, and disease onset might motivate smoking cessation, thus residual confounding and reverse causality might bias results. We aimed to assess the evidence of a causal relationship between smoking intensity and cause-specific as well as all-cause-mortality using Mendelian randomization analyses. Methods We included 56 019 participants from the Norwegian HUNT2 Study and 337 103 participants from UK Biobank, linked to national registry data on causes of death. We estimated associations of self-reported smoking as well as the genetic variant rs1051730 as an instrument for smoking intensity with all-cause and cause-specific mortality. We subsequently meta-analysed the results from the two cohorts. Results Each effect allele of the rs1051730 was associated with a 9% increased hazard of all-cause mortality [95% confidence interval (CI) 6–11] among ever smokers. Effect alleles were also associated with death by neoplasms [hazard ratio (HR) 1.11, 95% CI 1.06–1.15], circulatory diseases (HR 1.06, 95% CI 1.01–1.11) and respiratory diseases (HR 1.15, 95% CI 1.05–1.26) among ever smokers. The association was stronger among ever than never smokers for all-cause mortality (p < 0.001), neoplasms (p = 0.001) and respiratory diseases (p = 0.038). Conclusions Our results indicate a causal effect of smoking intensity on all-cause mortality and death by neoplasms and respiratory diseases. There was weaker evidence of a causal effect of smoking intensity on death by circulatory diseases.

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22

Jain, Sanjay Kumar, and Rajesh Kumar Jain. "Review of FDA Warning Letters to Pharmaceuticals: Cause and Effect Analysis." Research Journal of Pharmacy and Technology 11, no. 7 (2018): 3219. http://dx.doi.org/10.5958/0974-360x.2018.00592.9.

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23

Xiaojing, Lu. "The Cause and Effect Analysis of the Melamine Incident in China." Asian Journal of Agricultural Research 5, no. 3 (April 15, 2011): 176–85. http://dx.doi.org/10.3923/ajar.2011.176.185.

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24

Levina, Olga. "Design of Decision Service Using Cause-and-Effect Business Process Analysis." Scientific Journal of Riga Technical University. Computer Sciences 43, no. 1 (January 1, 2011): 19–26. http://dx.doi.org/10.2478/v10143-011-0003-8.

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Design of Decision Service Using Cause-and-Effect Business Process AnalysisBusiness processes are executed according to a specific business logic that can be formulated as business rules. Often decisions are taken during the course of the process to fulfill conditions or exceptions leading to process variations. These decisions are meant to support business process operations and are composed of a set of business rules, additionally being based on a solid process relevant knowledge and know-how. These processes are here referred to as decision knowledge intensive processes. In this paper the question of how business rules and consequently operational decisions can be derived from the business process model is addressed. The therefore necessary business process analysis and decision identification are performed using the cause-and-effect approach. Service-oriented implementation of the found decisions is suggested and outlined.

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25

White, Andrew A., James W. Pichert, Sandra H. Bledsoe, Cindy Irwin, and Stephen S. Entman. "Cause and Effect Analysis of Closed Claims in Obstetrics and Gynecology." Obstetrics & Gynecology 105, no. 5, Part 1 (May 2005): 1031–38. http://dx.doi.org/10.1097/01.aog.0000158864.09443.77.

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26

Serna, Alfredo, David Ramos, Enrique Garcia-Angosto, Antonio-Javier Garcia-Sanchez, Maria A. Chans, Jose M. Benedicto-Orovitg, Vicente Puchades-Puchades, and Juan F. Mata-Colodro. "Optimization of CT protocols using cause-and-effect analysis of outliers." Physica Medica 55 (November 2018): 1–7. http://dx.doi.org/10.1016/j.ejmp.2018.10.010.

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27

Zhang, Mengmeng, Honghui Chen, and Junxian Liu. "Cause–Effect Analysis for Multiple Misalignment Symptoms in Misalignment Correction Stage." Arabian Journal for Science and Engineering 44, no. 3 (July 26, 2018): 2729–35. http://dx.doi.org/10.1007/s13369-018-3450-5.

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28

Roca, J. L. "Cause-effect failure analysis for measure channels with computer process units." Microelectronics Reliability 34, no. 11 (November 1994): 1811–19. http://dx.doi.org/10.1016/0026-2714(94)90135-x.

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29

Rudenko, Anna, and Oleksandr Zviriaka Zviriaka. "Analysis of cause-effect relationship of hip dysplasia in pre-school children." Слобожанський науково-спортивний вісник 50, no. 6 (December 31, 2015): 155–59. http://dx.doi.org/10.15391/snsv.2015-6.028.

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30

TAKEUCHI, Shigeo, Shin'ichi MAGAINO, Koichi KOBAYAKAWA, and Yuichi SATO. "Cause of Memory Effect Observed in Alkaline Secondary Batteries–AC Impedance Analysis." Electrochemistry 68, no. 12 (December 5, 2000): 977–83. http://dx.doi.org/10.5796/electrochemistry.68.977.

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31

Yu, Yu, Shengfei Wang, and Fenglei Niu. "Analysis of common cause failure effect on system reliability in seismic PSA." Progress in Nuclear Energy 75 (August 2014): 158–67. http://dx.doi.org/10.1016/j.pnucene.2014.04.020.

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32

Rotshtein, Alexander P., Morton Posner, and Hanna B. Rakytyanska. "Cause and effect analysis by fuzzy relational equations and a genetic algorithm." Reliability Engineering & System Safety 91, no. 9 (September 2006): 1095–101. http://dx.doi.org/10.1016/j.ress.2005.11.041.

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33

Pinna, Andrea, Nicola Soranzo, and Alberto de la Fuente. "From Knockouts to Networks: Establishing Direct Cause-Effect Relationships through Graph Analysis." PLoS ONE 5, no. 10 (October 11, 2010): e12912. http://dx.doi.org/10.1371/journal.pone.0012912.

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34

Kiani Mavi, Reza, and Craig Standing. "Cause and effect analysis of business intelligence (BI) benefits with fuzzy DEMATEL." Knowledge Management Research & Practice 16, no. 2 (March 22, 2018): 245–57. http://dx.doi.org/10.1080/14778238.2018.1451234.

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35

Hyun, Nak-seung Patrick, and Erik I. Verriest. "Cause versus Effect in Hybrid Systems: A Rigorous Non-standard Analysis Approach." IFAC-PapersOnLine 48, no. 27 (2015): 129–34. http://dx.doi.org/10.1016/j.ifacol.2015.11.164.

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36

Roca, J. L. "Cause-effect alarm failure analysis for nuclear reactor protection system EDM modules." Microelectronics Reliability 35, no. 2 (February 1995): 241–49. http://dx.doi.org/10.1016/0026-2714(95)90089-9.

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37

Oktaviani, Oki, Budi Susetyo, and Bambang Purwoko Kusumo Bintoro. "Risk Management Model using Cause and Effect Analysis in Industrial Building Project." International Journal of Research and Review 8, no. 8 (August 17, 2021): 227–35. http://dx.doi.org/10.52403/ijrr.20210832.

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The purpose of this study is to determine the main potential risks that occur in the design planning stage of a construction project, especially in an Industrial Building project. Based on the available literature and the experiences of other authors and practitioners, a list of potential risks was developed. Risks events were initially categorized into technical, project management, commercial and external categories. Risks are evaluated by professional practitioners who have experience in construction projects. The evaluation includes the expected likelihood of the event and its impact on changes in scope of work. This study considers each risk among owners, consultants, contractors and others. The data collected were analyzed qualitatively and quantitatively to assess the severity and impact of the event. The recommended responses to major risks are introduced in this study. Keywords: risk management model, planning stage, industrial building.

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38

Zusryn, Alyta Shabrina. "CAUSE AND EFFECT ANALYSIS PENYALURAN KREDIT P2P LENDING PADA UMKM DI INDONESIA." Jurnal Orientasi Bisnis dan Entrepreneurship (JOBS) 1, no. 2 (January 23, 2021): 83–96. http://dx.doi.org/10.33476/jobs.v1i2.1689.

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Pertumbuhan ekonomi nasional dipengaruhi oleh berkembangnya Usaha Mikro, Kecil, dan Menengah (UMKM). P2P Lending merupakan salah satu bentuk solusi pembiayaan berbasiskan teknologi bagi UMKM. Tujuan pada penelitian ini adalah menganalisis sebab dan dampak penyaluran kredit P2P lending pada UMKM. Analisis data pada penelitian ini menggunakan content analysis. Selain itu, pada penelitian ini melibatkan diagram “fishbone” untuk mendeskripsikan sebab dan dampak pada penyaluran kredit P2P lending. Hasil pada penelitian ini menunjukkan bahwa faktor-faktor yang menyebabkan lambatnya perkembangan kredit P2P lending pada UMKM di Indonesia adanya adanya risiko gagal bayar tinggi pada kredit P2P lending, masih banyak pelaku usaha yang unbankable, masih kurangnya kepercayaan investor, kurangnya kesiapan regulasi, dan kurangnya sosialisasi terkait lembaga penjaminan kredit. Oleh karena itu, solusi pada permasalahan tersebut adalah pembentukan kelompok peminjam dan adanya sistem simpanan rutin untuk membantu jika ada peserta yang gagal bayar. Selain itu diperlukan koordinasi antar lembaga pemerintahan dalam mempersiapkan regulasi terkait keamanan kegiatan bisnis P2P lending dan kerahasiaan data baik pada investor maupun peminjam.

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Andronova, Natalia G., and Michael E. Schlesinger. "The application of cause-and-effect analysis to mathematical models of geophysical phenomena 2. Stability analysis." Journal of Geophysical Research 97, no. D5 (1992): 5911. http://dx.doi.org/10.1029/92jd00238.

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40

Hazarika, Kalpana, and G. L. Pahuja. "Effect of Common Cause Failures on Redundancy Optimization." Applied Mechanics and Materials 592-594 (July 2014): 2491–95. http://dx.doi.org/10.4028/www.scientific.net/amm.592-594.2491.

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Redundancy allocation (RA) is one of the important approaches to improve system reliability by incorporating generally s-identical and s-independent components. However in the presence of common cause failures (CCF), a rarely studied problem as Redundancy optimization, the optimized system reliability may be adversely affected. In this paper the effect of CCF on Reliability Optimization using Redundancy Allocation has been expedited. The paper is implemented in two steps. Step1 involves obtaining the optimized solution of the problem using existing techniques. In the next step, modifications are done to compute optimized system reliability under CCF. The optimized reliability expression is modified to depict the effect of CCF. The work is extended to complex network analysis & implicit method has been used to do this work. The adverse effect of CCF on system reliability has also been demonstrated quantitatively with the help of example.

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41

Collins, Scott L., Susan M. Glenn, and David J. Gibson. "Experimental Analysis of Intermediate Disturbance and Initial Floristic Composition: Decoupling Cause and Effect." Ecology 76, no. 2 (March 1995): 486–92. http://dx.doi.org/10.2307/1941207.

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42

van der Hoff, Quay, and Ansie Harding. "Cause–effect analysis: improvement of a first year engineering students’ calculus teaching model." International Journal of Mathematical Education in Science and Technology 48, no. 1 (June 28, 2016): 16–30. http://dx.doi.org/10.1080/0020739x.2016.1199058.

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43

Wall, J., O. K. Aeddula, and T. Larsson. "DATA ANALYSIS METHOD SUPPORTING CAUSE AND EFFECT STUDIES IN PRODUCT-SERVICE SYSTEM DEVELOPMENT." Proceedings of the Design Society: DESIGN Conference 1 (May 2020): 461–70. http://dx.doi.org/10.1017/dsd.2020.123.

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AbstractA data analysis method aiming to support cause and effect analysis in design exploration studies is presented. The method clusters and aggregates effects of multiple design variables based on the structural hierarchy of the evaluated system. The resulting dataset is intended as input to a visualization construct based on colour-coding CAD models. The proposed method is exemplified in a case study showing that the predictive capability of the created, clustered, dataset is comparable to the original, unmodified, one.

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44

Becker, Matthias, Dakshina Dasari, Saad Mubeen, Moris Behnam, and Thomas Nolte. "End-to-end timing analysis of cause-effect chains in automotive embedded systems." Journal of Systems Architecture 80 (October 2017): 104–13. http://dx.doi.org/10.1016/j.sysarc.2017.09.004.

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45

Dürr, Marco, Georg Von Der Brüggen, Kuan-Hsun Chen, and Jian-Jia Chen. "End-to-End Timing Analysis of Sporadic Cause-Effect Chains in Distributed Systems." ACM Transactions on Embedded Computing Systems 18, no. 5s (October 19, 2019): 1–24. http://dx.doi.org/10.1145/3358181.

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46

Rösslein, Matthias, John T. Elliott, Marc Salit, Elijah J. Petersen, Cordula Hirsch, Harald F. Krug, and Peter Wick. "Use of Cause-and-Effect Analysis to Design a High-Quality Nanocytotoxicology Assay." Chemical Research in Toxicology 28, no. 1 (January 6, 2015): 21–30. http://dx.doi.org/10.1021/tx500327y.

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47

Nagasaki, Shinya. "Cause–effect analysis on Fukushima accident reports – What did McMaster undergraduate students learn?" Journal of Nuclear Science and Technology 53, no. 6 (August 5, 2015): 756–65. http://dx.doi.org/10.1080/00223131.2015.1071290.

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48

Deng, Yongzhi, Zongquan Sun, Jie Ma, and Hugh S. Paterson. "Semi-skeletonized internal mammary grafts and phrenic nerve injury: Cause-and-effect analysis." Journal of Huazhong University of Science and Technology 26, no. 4 (September 2006): 455–59. http://dx.doi.org/10.1007/s11596-006-0420-z.

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49

晋, 丝雨. "Exploring the Effect of Root Cause Analysis Method in Improving Outpatient Nursing Teaching." Nursing Science 10, no. 03 (2021): 229–34. http://dx.doi.org/10.12677/ns.2021.103038.

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50

Stankov, Ivana, Andres F. Useche, Jose D. Meisel, Felipe Montes, Lidia MO Morais, Amelia AL Friche, Brent A. Langellier, et al. "Using cause-effect graphs to elicit expert knowledge for cross-impact balance analysis." MethodsX 8 (2021): 101492. http://dx.doi.org/10.1016/j.mex.2021.101492.

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