{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T22:12:30Z","timestamp":1742940750341,"version":"3.40.3"},"publisher-location":"Cham","reference-count":55,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030666606"},{"type":"electronic","value":"9783030666613"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T00:00:00Z","timestamp":1617580800000},"content-version":"vor","delay-in-days":94,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100013081","name":"Augsburg University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013081","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016135","name":"Universit\u00e4t Passau","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016135","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002714","name":"Albert-Ludwigs-Universit\u00e4t Freiburg","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002714","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009091","name":"Justus Liebig Universit\u00e4t Gie\u00dfen","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009091","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006764","name":"Technische Universit\u00e4t Berlin","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006764","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Universit\u00e4t Bayreuth"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In a networked world, companies depend on fast and smart decisions, especially when it comes to reacting to external change. With the wealth of data available today, smart decisions can increasingly be based on data analysis and be supported by IT systems that leverage AI. A global pandemic brings external change to an unprecedented level of unpredictability and severity of impact. Resilience therefore becomes an essential factor in most decisions when aiming at making and keeping them smart. In this chapter, we study the characteristics of resilient systems and test them with four use cases in a wide-ranging set of application areas. In all use cases, we highlight how AI can be used for data analysis to make smart decisions and contribute to the resilience of systems.<\/jats:p>","DOI":"10.1007\/978-3-030-66661-3_13","type":"book-chapter","created":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T18:42:15Z","timestamp":1619462535000},"page":"221-239","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Data Analytics for Smart Decision-Making and Resilient Systems"],"prefix":"10.1007","author":[{"given":"Benjamin","family":"Blau","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Clemens van","family":"Dinther","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christoph M.","family":"Flath","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rico","family":"Knapper","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Rolli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,4,5]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-97436-1","volume-title":"Artificial Intelligence for Business: Springer Briefs in Business","author":"R Akerkar","year":"2019","unstructured":"Akerkar, R. 2019. Artificial Intelligence for Business: Springer Briefs in Business. Springer."},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Araujo, Theo, et al. 2020. \u201cIn AI we trust? Perceptions about automated decision-making by artificial intelligence\u201d. AI & SOCIETY\nISSN: 1435-5655. https:\/\/doi.org\/10.1007\/s00146-019-00931-w.","DOI":"10.1007\/s00146-019-00931-w"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Bader, Verena, and Stephan Kaiser. 2019. \u201cAlgorithmic decision-making? The user interface and its role for human involvement in decisions supported by artificial intelligence\u201d. Organization 26 (5): 655\u2013672. ISSN: 1350-5084. https:\/\/doi.org\/10.1177\/1350508419855714.","DOI":"10.1177\/1350508419855714"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Bansal, Gagan, et al. 2019. \u201cUpdates in human-AI teams: Understanding and addressing the performance\/compatibility tradeoff\u201d. In Proceedings of the AAAI Conference on Artificial Intelligence, 33:2429\u20132437.","DOI":"10.1609\/aaai.v33i01.33012429"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Blau, B., et al. 2009. \u201cService Value Networks\u201d. In 2009 IEEE Conference on Commerce and Enterprise Computing, 194\u2013201.","DOI":"10.1109\/CEC.2009.64"},{"key":"13_CR6","unstructured":"Blei, David M, Andrew Y Ng, and Michael I Jordan. 2003. \u201cLatent Dirichlet allocation\u201d. Journal of machine Learning research 3 (Jan): 993\u20131022."},{"key":"13_CR7","doi-asserted-by":"publisher","unstructured":"Burton, Jason W., Mari-Klara Stein, and Tina Blegind Jensen. 2020. \u201cA systematic review of algorithm aversion in augmented decision making\u201d. Journal of Behavioral Decision Making 33 (2): 220\u2013239. ISSN: 0894-3257. https:\/\/doi.org\/10.1002\/bdm.2155.","DOI":"10.1002\/bdm.2155"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Cheema-Fox, Alexander, et al. 2020. \u201cCorporate Resilience and Response During COVID-19\u201d. Harvard Business School Accounting and Management Unit Working Paper No. 20-108.","DOI":"10.2139\/ssrn.3578167"},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Croston, J. D. 1972. \u201cForecasting and Stock Control for Intermittent Demands\u201d. Operational Research Quarterly (1970-1977) 23 (3): 289. ISSN: 00303623. https:\/\/doi.org\/10.2307\/3007885.","DOI":"10.2307\/3007885"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Diener, Michael, Leopold Blessing, and Rappel Nina. 2016. \u201cTackling the Cloud Adoption Dilemma - A User Centric Concept to Control Cloud Migration Processes by Using Machine Learning Technologies\u201d. In International Conference on Availability, Reliability and Security (ARES).","DOI":"10.1109\/ARES.2016.39"},{"issue":"1","key":"13_CR11","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1057\/palgrave.ejis.3000344","volume":"9","author":"J S Edwards","year":"2000","unstructured":"Edwards, J. S., Y. Duan, and P.C. Robins. 2000. \u201cAn analysis of expert systems for business decision making at different levels and in different roles\u201d. European Journal of Information Systems 9 (1): 36\u201346.","journal-title":"European Journal of Information Systems"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Fan, Zhi-Ping, Yu-Jie Che, and Zhen-Yu Chen. 2017. \u201cProduct sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis\u201d. Journal of Business Research 74:90\u2013100. ISSN: 0148-2963. https:\/\/doi.org\/10.1016\/j.jbusres.2017.01.010. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0148296317300231.","DOI":"10.1016\/j.jbusres.2017.01.010"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Feng, Qi, and J George Shanthikumar. 2018. \u201cHow research in production and operations management may evolve in the era of big data\u201d. Production and Operations Management27 (9): 1670\u20131684.","DOI":"10.1111\/poms.12836"},{"key":"13_CR14","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.compind.2017.09.003","volume":"94","author":"Christoph M Flath","year":"2017","unstructured":"Flath, Christoph M., and Nikolai Stein. 2017. \u201cTowards a Data Science Toolbox for Industrial Analytics Applications\u201d. Computers in Industry 94:16\u201325.","journal-title":"Computers in Industry"},{"issue":"5","key":"13_CR15","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1579\/0044-7447-31.5.437","volume":"31","author":"Carl Folke","year":"2002","unstructured":"Folke, Carl, et al. 2002. \u201cResilience and Sustainable Development: Building Adaptive Capacity in a World of Transformations\u201d. AMBIO: A Journal of the Human Environment 31 (5): 437\u2013440. https:\/\/doi.org\/10.1579\/0044-7447-31.5.437.","journal-title":"AMBIO: A Journal of the Human Environment"},{"key":"13_CR16","unstructured":"Foxall, Gordon R. 2017. Advanced introduction to consumer behavior analysis. Elgar advanced introductions. Cheltenham, UK: Edward Elgar. ISBN: 1784716928."},{"key":"13_CR17","series-title":"by Jay Liebowitz","first-page":"29","volume-title":"Business Analytics","author":"Frank Stein","year":"2014","unstructured":"Frank Stein and Arnold Greenland. 2014. \u201cProducing Insights from Information through Analytics\u201d. In Business Analytics, ed. by Jay Liebowitz, 29\u201354. CRC Press Taylor and Francis Group."},{"key":"13_CR18","unstructured":"Friedman, Ted. 2009. \u201cRisks and Challenges in Data Migrations and Conversions\u201d. Retrieved from Gartner Research Portal."},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"Gholami, Mahdi Fahmideh, et al. 2017. \u201cChallenges in migrating legacy software systems to the cloud \u2014 an empirical study\u201d. Information Systems 67:100\u2013113. ISSN: 0306-4379. https:\/\/doi.org\/10.1016\/j.is.2017.03.008. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0306437917301564.","DOI":"10.1016\/j.is.2017.03.008"},{"key":"13_CR20","unstructured":"Gilbert, CHE, and Erric Hutto. 2014. \u201cVader: A parsimonious rule-based model for sentiment analysis of social media text\u201d. In Eighth International Conference on Weblogs and Social Media (ICWSM-14). Available at (20\/04\/16)\n\nhttp:\/\/comp.social.gatech.edu\/papers\/icwsm14.vader.hutto.pdf\n, 81:82."},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Golan, Maureen S., Laura H. Jernegan, and Igor Linkov. 2020. \u201cTrends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic\u201d. Environment systems & decisions: 1\u201322. https:\/\/doi.org\/10.1007\/s10669-020-09777-w.","DOI":"10.1007\/s10669-020-09777-w"},{"key":"13_CR22","unstructured":"Gunderson, Lance H., and C. S. Holling. 2002. Panarchy: Understanding transformations in human and natural systems. Washington, DC: Island Press. ISBN: 9781559638579."},{"key":"13_CR23","doi-asserted-by":"crossref","unstructured":"Hartmann, Nathaniel N., and Bruno Lussier. 2020. \u201cManaging the sales force through the unexpected exogenous COVID-19 crisis\u201d. Industrial Marketing Management 88:101\u2013111. ISSN: 0019-8501. https:\/\/doi.org\/10.1016\/j.indmarman.2020.05.005\nhttp:\/\/www.sciencedirect.com\/science\/article\/pii\/S0019850120302972.","DOI":"10.1016\/j.indmarman.2020.05.005"},{"key":"13_CR24","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1007\/s10021-001-0101-5","volume":"4","author":"C S Holling","year":"2001","unstructured":"Holling, C. S. 2001. \u201cUnderstanding the complexity of economic, ecological, and social systems\u201d. Ecosystems 4:390\u2013405. https:\/\/doi.org\/10.1007\/s10021-00-0101-5.","journal-title":"Ecosystems"},{"key":"13_CR25","doi-asserted-by":"publisher","first-page":"101922","DOI":"10.1016\/j.tre.2020.101922","volume":"136","author":"Dmitry Ivanov","year":"2020","unstructured":"Ivanov, Dmitry. 2020. \u201cPredicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19\/SARS-CoV-2) case\u201d. Transportation research. Part E, Logistics and transportation review 136:101922. https:\/\/doi.org\/10.1016\/j.tre.2020.101922.","journal-title":"Transportation research. Part E, Logistics and transportation review"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Jackson, Scott, and Timothy L. J. Ferris. 2013. \u201cResilience principles for engineered systems\u201d. Systems Engineering16 (2): 152\u2013164. doi: 10.1002\/sys21228. eprint: https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/sys.21228","DOI":"10.1002\/sys.21228"},{"key":"13_CR27","unstructured":"Kamar, Ece, Severin Hacker, and Eric Horvitz. 2012. \u201cCombining human and machine intelligence in large-scale crowdsourcing.\u201d In AAMAS. 12:467\u2013474."},{"key":"13_CR28","unstructured":"Kasparov, Garry. 2017. Deep thinking: where machine intelligence ends and human creativity begins. Hachette UK."},{"key":"13_CR29","unstructured":"Kiefer, Daniel, and Clemens van Dinther. 2020. \u201cDemand Forecasting Intermittent and Lumpy Time Series: Deep Learning a magic bullet?\u201d"},{"key":"13_CR30","unstructured":"Krenzer, Adrian, et al. 2019. \u201cAugmented Intelligence for Quality Control of Manual Assembly Processes using Industrial Wearable Systems\u201d. In Proceedings of the 40th International Conference on Information Systems (ICIS)."},{"key":"13_CR31","doi-asserted-by":"crossref","unstructured":"Kusiak, A., et al. 2000. \u201cAutonomous decision-making: a data mining approach\u201d. IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society 4 (4): 274\u2013284. ISSN: 1089-7771. https:\/\/doi.org\/10.1109\/4233.897059.","DOI":"10.1109\/4233.897059"},{"key":"13_CR32","unstructured":"Lakhmi C. Jain. 2009. \u201cAdvances in Decision Making\u201d. In Recent Advances in Decision Making, ed. by Janusz Kacprzyk et al., 1\u20136. Berlin, Heidelberg: Springer Berlin Heidelberg. ISBN: 978-3-642-02186-2."},{"key":"13_CR33","doi-asserted-by":"crossref","unstructured":"Lepenioti, Katerina, et al. 2020. \u201cPrescriptive analytics: Literature review and research challenges\u201d. International Journal of Information Management 50:57\u201370. ISSN: 02684012. https:\/\/doi.org\/10.1016\/j.ijinfomgt.2019.04.003.","DOI":"10.1016\/j.ijinfomgt.2019.04.003"},{"key":"13_CR34","doi-asserted-by":"crossref","unstructured":"Loureiro, A.L.D., V. L. Migu\u00e9is, and Lucas F.M. da Silva. 2018. \u201cExploring the use of deep neural networks for sales forecasting in fashion retail\u201d. Decision Support Systems114:81\u201393. ISSN: 01679236. https:\/\/doi.org\/10.1016\/j.dss.2018.08.010","DOI":"10.1016\/j.dss.2018.08.010"},{"key":"13_CR35","doi-asserted-by":"crossref","unstructured":"Manekar, S, and Pradeepini Gera. 2017. \u201cOpportunity and Challenges for Migrating Big Data Analytics in Cloud\u201d. IOP Conference Series: Materials Science and Engineering225 (): 012148. https:\/\/doi.org\/10.1088\/1757-899X\/225\/1\/012148.","DOI":"10.1088\/1757-899X\/225\/1\/012148"},{"key":"13_CR36","doi-asserted-by":"crossref","unstructured":"McKone, Kathleen E, and Elliott N Weiss. 2002. \u201cGuidelines for implementing predictive maintenance\u201d. Production and Operations Management11 (2): 109\u2013124.","DOI":"10.1111\/j.1937-5956.2002.tb00486.x"},{"key":"13_CR37","unstructured":"Oberdorf, Felix, et al. 2020. \u201cADR for Big-Data IT Artifact Development: An Escalation Management Example\u201d. In Proceedings of the 41st International Conference on Information Systems (ICIS)."},{"key":"13_CR38","doi-asserted-by":"crossref","unstructured":"Paul, H Yi, Ferdinand K Hui, and Daniel SW Ting. 2018. \u201cArtificial intelligence and radiology: collaboration is key\u201d. Journal of the American College of Radiology15 (5): 781\u2013783.","DOI":"10.1016\/j.jacr.2017.12.037"},{"key":"13_CR39","doi-asserted-by":"crossref","unstructured":"Peter R. Winters. 1960. \u201cForecasting Sales by Exponentially Weighted Moving Averages\u201d. Management Science6 (3): 324\u2013342.","DOI":"10.1287\/mnsc.6.3.324"},{"key":"13_CR40","doi-asserted-by":"crossref","unstructured":"Phillips-Wren, G., and L. Jain. 2006. \u201cKnowledge-based intelligent Information and Engineering Systems\u201d. Chap. Artificial Intelligence for Decision Making, ed. by Bogdan Gabrys, Robert J. Howlett, and Lakhmi Jain, 531\u2013536. Springer.","DOI":"10.1007\/11893004_69"},{"key":"13_CR41","doi-asserted-by":"crossref","unstructured":"Phillips-Wren, G. (2012).Phillips-Wren, Gloria. 2012. \u201cAI tools in Decision Making Support Systems: a review\u201d. International Journal on Artificial Intelligence Tools21 (02): 1240005. ISSN: 0218-2130. https:\/\/doi.org\/10.1142\/S0218213012400052.","DOI":"10.1142\/S0218213012400052"},{"key":"13_CR42","unstructured":"Pierre Haren and David Simchi-Levi. 2020. \u201cHow Coronavirus Could Impact the Global Supply Chain by Mid-March\u201d. Harvard Business Review2020 (03)."},{"key":"13_CR43","unstructured":"Ricardo, David. 1817. The Principles of Political Economy and Taxation. Reprint from 1926. London and Toronto: J.M. Dent\/Sons."},{"key":"13_CR44","doi-asserted-by":"crossref","unstructured":"Sharma, Amalesh, Anirban Adhikary, and Sourav Bikash Borah. 2020. \u201cCovid-19\u2032s impact on supply chain decisions: Strategic insights from NASDAQ 100 firms using Twitter data\u201d. Journal of Business Research117:443\u2013449. ISSN: 0148-2963. https:\/\/doi.org\/10.1016\/j.jbusres.2020.05.035. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0148296320303210.","DOI":"10.1016\/j.jbusres.2020.05.035"},{"key":"13_CR45","doi-asserted-by":"crossref","unstructured":"Shrestha, Yash Raj, Shiko M. Ben-Menahem, and Georg von Krogh. 2019. \u201cOrganizational Decision-Making Structures in the Age of Artificial Intelligence\u201d. California Management Review61 (4): 66\u201383. ISSN: 0008-1256. https:\/\/doi.org\/10.1177\/0008125619862257.","DOI":"10.1177\/0008125619862257"},{"key":"13_CR46","doi-asserted-by":"crossref","unstructured":"Stein, Nikolai, Jan Meller, and Christoph M Flath. 2018. \u201cBig data on the shop-floor: sensor-based decision-support for manual processes\u201d. Journal of Business Economics88 (5): 593\u2013616.","DOI":"10.1007\/s11573-017-0890-4"},{"key":"13_CR47","doi-asserted-by":"crossref","unstructured":"Strobl, Stefan, Mario Bernhart, and Thomas Grechenig. 2020. \u201cTowards a Topology for Legacy System Migration\u201d. In Proceedings of the IEEE\/ACM 42nd International Conference on Software Engineering Workshops, 586\u2013594. IC-SEW\u201920. Seoul, Republic of Korea: Association for Computing Machinery. ISBN: 9781450379632. https:\/\/doi.org\/10.1145\/3387940.3391476.","DOI":"10.1145\/3387940.3391476"},{"key":"13_CR48","unstructured":"Stubbs, Evan. 2014. \u201cBusiness Analytics: An Introduction\u201d. Chap. The Value of Business Analytics, ed. by Jay Liebowitz, 1\u201328. CRC Press, Taylor \/ Francis Group."},{"key":"13_CR49","doi-asserted-by":"crossref","unstructured":"Wang, Dakuo, et al. 2019. \u201cHuman-AI Collaboration in Data Science: Exploring Data Scientists\u2019 Perceptions of Automated AI\u201d. Proceedings of the ACM on Human-Computer Interaction3 (CSCW): 1\u201324.","DOI":"10.1145\/3359313"},{"key":"13_CR50","doi-asserted-by":"crossref","unstructured":"Wuest, Thorsten, Christopher Irgens, and Klaus-Dieter Thoben. 2014. \u201cAn approach to monitoring quality in manufacturing using supervised machine learning on product state data\u201d. Journal of Intelligent Manufacturing25 (5): 1167\u20131180.","DOI":"10.1007\/s10845-013-0761-y"},{"key":"13_CR51","doi-asserted-by":"crossref","unstructured":"Wuest, Thorsten, et al. 2016. \u201cMachine learning in manufacturing: advantages, challenges, and applications\u201d. Production & Manufacturing Research4 (1): 23\u201345.","DOI":"10.1080\/21693277.2016.1192517"},{"key":"13_CR52","doi-asserted-by":"crossref","unstructured":"Yin, Jianhua, and Jianyong Wang. 2014. \u201cA Dirichlet multinomial mixture model-based approach for short text clustering\u201d. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 233\u2013242.","DOI":"10.1145\/2623330.2623715"},{"key":"13_CR53","unstructured":"van Dinther, Clemens. 2007. Adaptive Bidding in Single-Sided Auctions Under Uncertainty: An Agent-based Approach in Market Engineering. Whitestein Series in Software Agent Technologies and Autonomic Computing. Basel: Birkha\u00e4user Verlag. ISBN: 978-3764380946."},{"key":"13_CR54","doi-asserted-by":"crossref","unstructured":"\u2013 2008. \u201cAgent-based Simulation for Research in Economics\u201d. In Handbook on Information Technology in Finance, ed. by Detlef Seese, Christof Weinhardt, and Frank Schlottmann, 421\u2013442. Berlin, Heidelberg: Springer Berlin Heidelberg. ISBN: 978-3-540-49487-4. https:\/\/doi.org\/10.1007\/978-3-540-49487-4_18.","DOI":"10.1007\/978-3-540-49487-4_18"},{"key":"13_CR55","unstructured":"van Dinther, Clemens, and Svenja Mauch. 2019. \u201cChancen der k\u00fcnstlichen Intelligenz zur Prognose im Mittelstand\u201d. Decision Growth, no. 3: 21\u201327. https:\/\/decision-growth.de\/Magazin\/\/catalogs\/Growth_Magazin_III\/growth-Magazin-Ausgabe-3\/pdf\/complete.pdf."}],"container-title":["Market Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-66661-3_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T11:35:01Z","timestamp":1710243301000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-66661-3_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030666606","9783030666613"],"references-count":55,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-66661-3_13","relation":{},"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"5 April 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}