{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T06:33:41Z","timestamp":1785738821103,"version":"3.56.0"},"reference-count":77,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T00:00:00Z","timestamp":1669161600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T00:00:00Z","timestamp":1669161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100010570","name":"Nieders\u00e4chsisches Ministerium f\u00fcr Wissenschaft und Kultur","doi-asserted-by":"publisher","award":["ZN3563"],"award-info":[{"award-number":["ZN3563"]}],"id":[{"id":"10.13039\/501100010570","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004115","name":"Gottfried Wilhelm Leibniz Universit\u00e4t Hannover","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004115","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Electron Markets"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The black-box nature of Artificial Intelligence (AI) models and their associated explainability limitations create a major adoption barrier. Explainable Artificial Intelligence (XAI) aims to make AI models more transparent to address this challenge. Researchers and practitioners apply XAI services to explore relationships in data, improve AI methods, justify AI decisions, and control AI technologies with the goals to improve knowledge about AI and address user needs. The market volume of XAI services has grown significantly. As a result, trustworthiness, reliability, transferability, fairness, and accessibility are required capabilities of XAI for a range of relevant stakeholders, including managers, regulators, users of XAI models, developers, and consumers. We contribute to theory and practice by deducing XAI archetypes and developing a user-centric decision support framework to identify the XAI services most suitable for the requirements of relevant stakeholders. Our decision tree is founded on a literature-based morphological box and a classification of real-world XAI services. Finally, we discussed archetypical business models of XAI services and exemplary use cases.<\/jats:p>","DOI":"10.1007\/s12525-022-00603-6","type":"journal-article","created":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T14:58:51Z","timestamp":1669301931000},"page":"2139-2158","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Decision support for efficient XAI services - A morphological analysis, business model archetypes, and a decision tree"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1331-3765","authenticated-orcid":false,"given":"Jana","family":"Gerlach","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Hoppe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah","family":"Jagels","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luisa","family":"Licker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael H.","family":"Breitner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,23]]},"reference":[{"key":"603_CR1","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138\u201352160. https:\/\/doi.org\/10.1109\/ACCESS.2018.2870052","journal-title":"IEEE Access"},{"issue":"3","key":"603_CR2","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1080\/08898489509525406","volume":"5","author":"DA Ahlburg","year":"1995","unstructured":"Ahlburg, D. A. (1995). Simple versus complex models: Evaluation, accuracy, and combining. Mathematical Population Studies, 5(3), 281\u2013292. https:\/\/doi.org\/10.1080\/08898489509525406","journal-title":"Mathematical Population Studies"},{"key":"603_CR3","doi-asserted-by":"publisher","first-page":"33132","DOI":"10.1109\/ACCESS.2021.3061368","volume":"9","author":"R Alamri","year":"2021","unstructured":"Alamri, R., & Alharbi, B. (2021). Explainable student performance prediction models: A systematic review. IEEE Access, 9, 33132\u201333143. https:\/\/doi.org\/10.1109\/ACCESS.2021.3061368","journal-title":"IEEE Access"},{"issue":"7","key":"603_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0130140","volume":"10","author":"S Bach","year":"2015","unstructured":"Bach, S., Binder, A., Montavon, G., Klauschen, F., M\u00fcller, K.-R., & Samek, W. (2015). On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS One, 10(7), 1\u201346. https:\/\/doi.org\/10.1371\/journal.pone.0130140","journal-title":"PLoS One"},{"key":"603_CR5","doi-asserted-by":"publisher","unstructured":"Barocas, S., Selbst, A.D., & Raghavan, M. (2020). The hidden assumptions behind counterfactual explanations and principal reasons.\u00a0Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency.\u00a0https:\/\/doi.org\/10.1145\/3351095.3372830","DOI":"10.1145\/3351095.3372830"},{"key":"603_CR6","doi-asserted-by":"publisher","unstructured":"Barredo Arrieta, A., D\u00edaz-Rodr\u00edguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garc\u00eda, S., Gil-L\u00f3pez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Information fusion, 58, 82\u2013115. https:\/\/doi.org\/10.48550\/arXiv.1910.10045","DOI":"10.48550\/arXiv.1910.10045"},{"key":"603_CR7","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/s12599-009-0044-5","volume":"1","author":"J Becker","year":"2009","unstructured":"Becker, J., Knackstedt, R., & P\u00f6ppelbu\u00df, J. (2009). Developing maturity models for IT management. Business & Information Systems Engineering, 1, 213\u2013222. https:\/\/doi.org\/10.1007\/s12599-009-0044-5","journal-title":"Business & Information Systems Engineering"},{"key":"603_CR8","doi-asserted-by":"publisher","unstructured":"Bennetot, A., Laurent, J.-L., Chatila, R., & D\u00edaz-Rodr\u00edguez, N. (2019). Towards explainable neural-symbolic visual reasoning. Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China.\u00a0https:\/\/doi.org\/10.48550\/arXiv.1909.09065","DOI":"10.48550\/arXiv.1909.09065"},{"issue":"4","key":"603_CR9","first-page":"17","volume":"25","author":"AC Boynton","year":"1984","unstructured":"Boynton, A. C., & Zmud, R. W. (1984). An assessment of critical success factors. Sloan Management Review, 25(4), 17\u201327.","journal-title":"Sloan Management Review"},{"issue":"1","key":"603_CR10","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Machine Learning"},{"key":"603_CR11","doi-asserted-by":"publisher","unstructured":"Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183\u2013186. https:\/\/doi.org\/10.1126\/science.aal4230","DOI":"10.1126\/science.aal4230"},{"key":"603_CR12","doi-asserted-by":"publisher","unstructured":"Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019). Machine learning interpretability: A survey on methods and metrics. Electronics, 8(8), 832. https:\/\/doi.org\/10.3390\/electronics8080832","DOI":"10.3390\/electronics8080832"},{"key":"603_CR13","doi-asserted-by":"publisher","unstructured":"Crupi, R., Castelnovo, A., Regoli, D., & Gonz\u00e1lez, B.S. (2021). Counterfactual explanations as interventions in latent space. \u00a0https:\/\/doi.org\/10.48550\/arXiv.2106.07754","DOI":"10.48550\/arXiv.2106.07754"},{"key":"603_CR14","doi-asserted-by":"publisher","first-page":"#100001","DOI":"10.1016\/j.health.2021.100001","volume":"1","author":"F Curia","year":"2021","unstructured":"Curia, F. (2021). Features and explainable methods for cytokines analysis of dry eye disease in HIV infected patients. Healthcare Analytics, 1, #100001. https:\/\/doi.org\/10.1016\/j.health.2021.100001","journal-title":"Healthcare Analytics"},{"key":"603_CR15","unstructured":"Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. Machine Learning, 1\u201313. Available at: http:\/\/arxiv.org\/abs\/1702.08608. Accessed 31 May 2022."},{"key":"603_CR16","doi-asserted-by":"crossref","unstructured":"F\u00f6rster, M., H\u00fchn, P., Klier, M., & Kluge, K. (2021). Capturing users\u2019 reality: a novel approach to generate. Coherent counterfactual explanations. Proceedings of the 54th Hawaii International Conference on System Sciences, Maui, USA (virtual).","DOI":"10.24251\/HICSS.2021.155"},{"key":"603_CR17","doi-asserted-by":"crossref","unstructured":"Gerlings, J., Shollo, A., & Constantiou, I. (2021). Reviewing the need for explainable artificial intelligence (xAI). Proceedings of the 54th Hawaiian International Conference on System Sciences, Maui, USA (virtual).","DOI":"10.24251\/HICSS.2021.156"},{"key":"603_CR18","doi-asserted-by":"publisher","unstructured":"Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018). Explaining explanations: an overview of interpretability of machine learning. Proceedings of the IEEE 5th International Conference on Data Science and Advanced Analytics, Turin, Italy.\u00a0https:\/\/doi.org\/10.1109\/DSAA.2018.00018","DOI":"10.1109\/DSAA.2018.00018"},{"issue":"1","key":"603_CR19","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1136\/amiajnl-2011-000089","volume":"19","author":"K Goddard","year":"2012","unstructured":"Goddard, K., Roudsari, A., & Wyatt, J. C. (2012). Automation bias: A systematic review of frequency, effect mediators, and mitigators. Journal of the American Medical Informatics Association, 19(1), 121\u2013127. https:\/\/doi.org\/10.1136\/amiajnl-2011-000089","journal-title":"Journal of the American Medical Informatics Association"},{"issue":"5","key":"603_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3236009","volume":"51","author":"R Guidotti","year":"2019","unstructured":"Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2019). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 1\u201342. https:\/\/doi.org\/10.1145\/3236009","journal-title":"ACM Computing Surveys"},{"key":"603_CR21","unstructured":"Haag, F., Hopf, K., Menelau Vasconcelos, P., & Staake, T. (2022). Augmented cross-selling through explainable AI-A case from energy retailing. Proceedings of the 30th European Conference on Information Systems, Timisoara, Romania."},{"issue":"4","key":"603_CR22","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1177\/0008125619864925","volume":"61","author":"M Haenlein","year":"2019","unstructured":"Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5\u201314. https:\/\/doi.org\/10.1177\/0008125619864925","journal-title":"California Management Review"},{"issue":"6","key":"603_CR23","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1080\/21681163.2021.1901784","volume":"9","author":"H Hakkoum","year":"2021","unstructured":"Hakkoum, H., Idri, A., & Abnane, I. (2021). Assessing and comparing interpretability techniques for artificial neural networks breast Cancer classification. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 9(6), 587\u2013599. https:\/\/doi.org\/10.1080\/21681163.2021.1901784","journal-title":"Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization"},{"key":"603_CR24","unstructured":"Hamm, P., Wittmann, H. F., & Klesel, M. (2021). Explain it to me and I will use it: a proposal on the impact of explainable AI on use behavior. Proceedings of the 42nd International Conference on Information Systems, Austin, USA."},{"key":"603_CR25","unstructured":"Hemmer, P., Schemmer, M., Riefle, L., Rosellen, N., V\u00f6ssing, M., & Kuehl, N. (2022). Factors that influence the adoption of human-AI collaboration in clinical decision-making. Proceedings of the 30th European Conference on Information Systems, Timisoara, Romania."},{"key":"603_CR26","unstructured":"HLEG-AI. (2019). Ethics guidelines for trustworthy artificial intelligence. Independent High-Level Expert Group on Artificial Intelligence set up by the European Commission. Available at: https:\/\/www.aepd.es\/sites\/default\/files\/2019-12\/ai-ethics-guidelines.pdf. Accessed 31 May 2022."},{"issue":"7","key":"603_CR27","doi-asserted-by":"publisher","first-page":"2398","DOI":"10.1109\/JBHI.2021.3060997","volume":"25","author":"P Ivaturi","year":"2021","unstructured":"Ivaturi, P., Gadaleta, M., Pandey, A. C., Pazzani, M., Steinhubl, S. R., & Quer, G. (2021). A comprehensive explanation framework for biomedical time series classification. IEEE Journal of Biomedical and Health Informatics, 25(7), 2398\u20132408. https:\/\/doi.org\/10.1109\/JBHI.2021.3060997","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"603_CR28","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s10100-017-0479-6","volume":"26","author":"B Kami\u0144ski","year":"2018","unstructured":"Kami\u0144ski, B., Jakubczyk, M., & Szufel, P. (2018). A framework for sensitivity analysis of decision trees. Central European Journal of Operations Research, 26, 135\u2013159. https:\/\/doi.org\/10.1007\/s10100-017-0479-6","journal-title":"Central European Journal of Operations Research"},{"issue":"1","key":"603_CR29","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.bushor.2018.08.004","volume":"62","author":"A Kaplan","year":"2019","unstructured":"Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who\u2019s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15\u201325. https:\/\/doi.org\/10.1016\/j.bushor.2018.08.004","journal-title":"Business Horizons"},{"key":"603_CR30","doi-asserted-by":"publisher","DOI":"10.1002\/9780470316801","volume-title":"Finding groups in data","author":"L Kaufman","year":"1990","unstructured":"Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data. Wiley & Sons."},{"key":"603_CR31","doi-asserted-by":"publisher","first-page":"32328","DOI":"10.1109\/ACCESS.2018.2837692","volume":"6","author":"MG Kibria","year":"2018","unstructured":"Kibria, M. G., Nguyen, K., Villardi, G. P., Zhao, O., Ishizu, K., & Kojima, F. (2018). Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE Access, 6, 32328\u201332338. https:\/\/doi.org\/10.1109\/ACCESS.2018.2837692","journal-title":"IEEE Access"},{"key":"603_CR32","unstructured":"Kim, T. W. (2018). Explainable artificial intelligence (XAI), the goodness criteria and the grasp-ability test. ArXiv,1\u20137. Available at: http:\/\/arxiv.org\/abs\/1810.09598. Accessed 31 May 2022."},{"key":"603_CR33","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s11147-021-09178-4","volume":"24","author":"C Koziol","year":"2021","unstructured":"Koziol, C., & Weitz, S. (2021). Does model complexity improve pricing accuracy? The case of CoCos. Review of Derivatives Research, 24, 261\u2013284. https:\/\/doi.org\/10.1007\/s11147-021-09178-4","journal-title":"Review of Derivatives Research"},{"key":"603_CR34","unstructured":"Kridel, D., Dineen, J., Dolk, D., & Castillo, D. (2020). Model interpretation and Explainability: towards creating transparency in prediction models. Proceedings of the 53th Hawaii International Conference on System Sciences, Maui, USA."},{"key":"603_CR35","doi-asserted-by":"publisher","unstructured":"Kundisch, D., Muntermann, J., Oberl\u00e4nder, A.M., Rau, D., R\u00f6glinger, M., Schoormann, T., & Szopinski, D. (2021). An update for taxonomy designers. Business & Information Systems Engineering. Online first. https:\/\/doi.org\/10.1007\/s12599-021-00723-x.","DOI":"10.1007\/s12599-021-00723-x"},{"key":"603_CR36","doi-asserted-by":"publisher","unstructured":"Li, X. -H., Cao, C. C., Shi, Y., Bai, W., Gao, H., Qiu, L., Wang, C., Gao,Y., Zhang, S., Xue, X., & Chen, L. (2020). A survey of data-driven and knowledge-aware explainable AI. IEEE Transactions on Knowledge and Data Engineering, 34(1), 29\u201349. https:\/\/doi.org\/10.1109\/TKDE.2020.2983930","DOI":"10.1109\/TKDE.2020.2983930"},{"issue":"1","key":"603_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/e23010018","volume":"23","author":"P Linardatos","year":"2021","unstructured":"Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2021). Explainable AI: A review of machine learning interpretability methods. Entropy, 23(1), 1\u201345. https:\/\/doi.org\/10.3390\/e23010018","journal-title":"Entropy"},{"issue":"10","key":"603_CR38","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1145\/3233231","volume":"61","author":"ZC Lipton","year":"2018","unstructured":"Lipton, Z. C. (2018). The mythos of model interpretability. Communications of the ACM, 61(10), 36\u201343. https:\/\/doi.org\/10.1145\/3233231","journal-title":"Communications of the ACM"},{"key":"603_CR39","doi-asserted-by":"crossref","unstructured":"Lockey, S., Gillespie, N., Holm, D., & Someh, I. A. (2021). A review of trust in artificial intelligence: challenges, vulnerabilities and future directions. Proceedings of the 54th Hawaii International Conference on System Sciences, Maui, USA (virtual).","DOI":"10.24251\/HICSS.2021.664"},{"key":"603_CR40","unstructured":"Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Proceedings of the 31th Conference on Neural Information Processing Systems, Long Beach, USA."},{"key":"603_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jbi.2020.103655","volume":"113","author":"AF Markus","year":"2021","unstructured":"Markus, A. F., Kors, J. A., & Rijnbeek, P. R. (2021). The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies. Journal of Biomedical Informatics, 113, 1\u201311. https:\/\/doi.org\/10.1016\/j.jbi.2020.103655","journal-title":"Journal of Biomedical Informatics"},{"key":"603_CR42","doi-asserted-by":"publisher","unstructured":"McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H.,\u00a0Back, T., Chesus, M., Corrado, G. S., Darzi, A., Etemadi, M., Garcia-Vicente, F., Gilbert, F. J., Halling-Brown, M., Hassabis, D., Jansen, S., Karthikesalingam, A., Kelly, C. J., King, D., Ledsam, J. R., Melnick, D., Mostofi, H., Peng, L., Reicher, J. J., Romera-Paredes, B., Sidebottom, R., Suleyman, M., Tse, D., Young, K. C., De Fauw, J., & Shetty, S. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89\u201394.\u00a0https:\/\/doi.org\/10.1038\/s41586-019-1799-6","DOI":"10.1038\/s41586-019-1799-6"},{"key":"603_CR43","doi-asserted-by":"publisher","first-page":"#109160","DOI":"10.1016\/j.compositesb.2021.109160","volume":"224","author":"S Meister","year":"2021","unstructured":"Meister, S., Wermes, M., St\u00fcve, J., & Groves, R. M. (2021). Investigations on explainable artificial intelligence methods for the deep learning classification of fibre layup defect in the automated composite manufacturing. Composites Part B: Engineering, 224, #109160. https:\/\/doi.org\/10.1016\/j.compositesb.2021.109160","journal-title":"Composites Part B: Engineering"},{"issue":"1","key":"603_CR44","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1080\/10580530.2020.1849465","volume":"39","author":"C Meske","year":"2022","unstructured":"Meske, C., Bunde, E., Schneider, J., & Gersch, M. (2022). Explainable artificial intelligence: Objectives, stakeholders, and future research opportunities. Information Systems Management, 39(1), 53\u201363. https:\/\/doi.org\/10.1080\/10580530.2020.1849465","journal-title":"Information Systems Management"},{"issue":"3\u20134","key":"603_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3387166","volume":"11","author":"S Mohseni","year":"2021","unstructured":"Mohseni, S., Zarei, N., & Ragan, E. D. (2021). A multidisciplinary survey and framework for design and evaluation of explainable AI systems. ACM Transactions on Interactive Intelligent Systems, 11(3\u20134), 1\u201345. https:\/\/doi.org\/10.1145\/3387166","journal-title":"ACM Transactions on Interactive Intelligent Systems"},{"issue":"3","key":"603_CR46","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1057\/ejis.2012.26","volume":"22","author":"RC Nickerson","year":"2013","unstructured":"Nickerson, R. C., Varshney, U., & Muntermann, J. (2013). A method for taxonomy development and its application in information systems. European Journal of Information Systems, 22(3), 336\u2013359.\u00a0https:\/\/doi.org\/10.1057\/ejis.2012.26","journal-title":"European Journal of Information Systems"},{"key":"603_CR47","unstructured":"Omdia. (2021). Revenues from the artificial intelligence (AI) software market worldwide from 2018 to 2025. Available at: https:\/\/www.statista.com\/statistics\/607716\/worldwide-artificial-intelligence-market-revenues\/. Accessed 31 May 2022."},{"issue":"1","key":"603_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.17705\/1CAIS.01601","volume":"16","author":"A Osterwalder","year":"2005","unstructured":"Osterwalder, A., Pigneur, Y., & Tucci, C. L. (2005). Clarifying business models: Origins, present, and future of the concept. Communications of the Association for Information Systems, 16(1), 1\u201325. https:\/\/doi.org\/10.17705\/1CAIS.01601","journal-title":"Communications of the Association for Information Systems"},{"key":"603_CR49","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.48550\/arXiv.1201.0490","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O.,\u00a0Blondel, M., M\u00fcller, A., Nothman, J., Louppe, G., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, \u00c9. (2011). Scikit-learn: Machine learning in python. Journal of machine learning research, 12, 2825\u20132830. https:\/\/doi.org\/10.48550\/arXiv.1201.0490","journal-title":"Journal of machine learning research"},{"key":"603_CR50","doi-asserted-by":"crossref","unstructured":"Polzer, A. K., Flei\u00df, J., Ebner, T., Kainz, P., Koeth, C., & Thalmann, S. (2022). Validation of AI-based information systems for sensitive use cases: using an XAI approach in pharmaceutical engineering. Proceedings of the 55th Hawaii International Conference on System Sciences, Maui, USA (virtual).","DOI":"10.24251\/HICSS.2022.186"},{"issue":"2","key":"603_CR51","doi-asserted-by":"publisher","first-page":"134","DOI":"10.2307\/3151680","volume":"20","author":"G Punj","year":"1983","unstructured":"Punj, G., & Stewart, D. W. (1983). Cluster analysis in marketing research: Review and suggestions for application. Journal of Marketing Research, 20(2), 134\u2013148. https:\/\/doi.org\/10.2307\/3151680","journal-title":"Journal of Marketing Research"},{"issue":"1","key":"603_CR52","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s11747-019-00710-5","volume":"48","author":"A Rai","year":"2020","unstructured":"Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48(1), 137\u2013141. https:\/\/doi.org\/10.1007\/s11747-019-00710-5","journal-title":"Journal of the Academy of Marketing Science"},{"issue":"1","key":"603_CR53","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1177\/194675671100300105","volume":"3","author":"T Ritchey","year":"2011","unstructured":"Ritchey, T. (2011). Modeling alternative futures with general morphological analysis. World Futures Review, 3(1), 83\u201394. https:\/\/doi.org\/10.1177\/194675671100300105","journal-title":"World Futures Review"},{"key":"603_CR54","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53\u201365. https:\/\/doi.org\/10.1016\/0377-0427(87)90125-7","journal-title":"Journal of Computational and Applied Mathematics"},{"key":"603_CR55","first-page":"40","volume":"2","author":"DW Ruck","year":"1990","unstructured":"Ruck, D. W., Rogers, S. K., & Kabrisky, M. (1990). Feature selection using a multilayer perceptron. Journal of Neural Network Computing, 2, 40\u201348.","journal-title":"Journal of Neural Network Computing"},{"key":"603_CR56","doi-asserted-by":"publisher","unstructured":"Saputra, D. M., Saputra, D., & Oswari, L. D. (2020). Effect of distance metrics in determining k-value in k-means clustering using elbow and silhouette method. Proceedings of the Sriwijaya International Conference on Information Technology and its Applications, Palembang, Indonesia. https:\/\/doi.org\/10.2991\/aisr.k.200424.051","DOI":"10.2991\/aisr.k.200424.051"},{"key":"603_CR57","unstructured":"Schoormann, T., Strobel, G., M\u00f6ller, F., & Petrik, D. (2021). Achieving sustainability with artificial intelligence-a survey of information systems research. Proceedings of the 42nd International Conference on Information Systems, Austin, USA."},{"key":"603_CR58","unstructured":"Sepp\u00e4l\u00e4, A., Birkstedt, T., & M\u00e4ntym\u00e4ki, M. (2021). From ethical AI principles to governed AI. Proceedings of the 42nd International Conference on Information Systems, Austin, USA."},{"key":"603_CR59","doi-asserted-by":"publisher","first-page":"#103457","DOI":"10.1016\/j.artint.2021.103457","volume":"294","author":"M Setzu","year":"2021","unstructured":"Setzu, M., Guidotti, R., Monreale, A., Turini, F., Pedreschi, D., & Giannotti, F. (2021). GLocalX - from local to global explanations of black box AI models. Artificial Intelligence, 294, #103457. https:\/\/doi.org\/10.1016\/j.artint.2021.103457","journal-title":"Artificial Intelligence"},{"key":"603_CR60","doi-asserted-by":"publisher","first-page":"443","DOI":"10.17705\/1CAIS.04839","volume":"48","author":"JC Sipior","year":"2021","unstructured":"Sipior, J. C., Lombardi, D. R., & Gabryelczyk, R. (2021). AI recruiting tools at ShipIt2Me.com. Communications of the Association for Information Systems, 48, 443\u2013455. https:\/\/doi.org\/10.17705\/1CAIS.04839","journal-title":"Communications of the Association for Information Systems"},{"key":"603_CR61","unstructured":"Statista. (2022). Size of explainable artificial intelligence (AI) market worldwide from 2020 to 2030. Available at: https:\/\/www.statista.com\/statistics\/1256246\/worldwide-explainable-ai-market-revenues\/. Accessed 31 May 2022."},{"key":"603_CR62","doi-asserted-by":"publisher","first-page":"11974","DOI":"10.1109\/ACCESS.2021.3051315","volume":"9","author":"I Stepin","year":"2021","unstructured":"Stepin, I., Alonso, J. M., Catala, A., & Pereira-Farina, M. (2021). A survey of contrastive and counterfactual explanation generation methods for explainable artificial intelligence. IEEE Access, 9, 11974\u201312001. https:\/\/doi.org\/10.1109\/ACCESS.2021.3051315","journal-title":"IEEE Access"},{"key":"603_CR63","unstructured":"Stroppiana Tabankov, S., & M\u00f6hlmannn, M. (2021). Artificial intelligence for in-flight services: how the lufthansa group managed explainability and accuracy concerns. Proceedings of the 42nd International Conference on Information Systems, Austin, USA."},{"issue":"1","key":"603_CR64","doi-asserted-by":"publisher","first-page":"112","DOI":"10.17705\/1CAIS.03706","volume":"37","author":"M Templier","year":"2015","unstructured":"Templier, M., & Par\u00e9, G. (2015). A framework for guiding and evaluating literature reviews. Communications of the Association for Information Systems, 37(1), 112\u2013137. https:\/\/doi.org\/10.17705\/1CAIS.03706","journal-title":"Communications of the Association for Information Systems"},{"issue":"2","key":"603_CR65","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1007\/s12525-020-00441-4","volume":"31","author":"S Thiebes","year":"2021","unstructured":"Thiebes, S., Lins, S., & Sunyaev, A. (2021). Trustworthy artificial intelligence. Electronic Markets, 31(2), 447\u2013464. https:\/\/doi.org\/10.1007\/s12525-020-00441-4","journal-title":"Electronic Markets"},{"key":"603_CR66","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.inffus.2021.05.009","volume":"76","author":"G Vilone","year":"2021","unstructured":"Vilone, G., & Longo, L. (2021). Notions of explainability and evaluation approaches for explainable artificial intelligence. Information Fusion, 76, 89\u2013106. https:\/\/doi.org\/10.1016\/j.inffus.2021.05.009","journal-title":"Information Fusion"},{"issue":"1","key":"603_CR67","doi-asserted-by":"publisher","first-page":"205","DOI":"10.17705\/1CAIS.03709","volume":"37","author":"J vom Brocke","year":"2015","unstructured":"vom Brocke, J., Simons, A., Riemer, K., Niehaves, B., Plattfaut, R., & Cleven, A. (2015). Standing on the shoulders of giants: Challenges and recommendations of literature search in information systems research. Communications of the Association for Information Systems, 37(1), 205\u2013224. https:\/\/doi.org\/10.17705\/1CAIS.03709","journal-title":"Communications of the Association for Information Systems"},{"key":"603_CR68","unstructured":"Wambsganss, T., Engel, C., & Fromm, H. (2021). Improving explainability and accuracy through feature engineering: a taxonomy of features in NLP-based machine learning. Proceedings of the 42nd International Conference on Information Systems, Austin, USA."},{"key":"603_CR69","unstructured":"Wang, H., Li, C., Gu, B., & Min, W. (2019). Does AI-based credit scoring improve financial inclusion? Evidence from online payday lending. Proceedings of the 40th International Conference on Information Systems, Munich, Germany."},{"key":"603_CR70","doi-asserted-by":"crossref","unstructured":"Wastensteiner, J., Weiss, T. M., Haag, F., & Hopf, K. (2021). Explainable AI for tailored electricity consumption feedback - an experimental evaluation of visualizations. Proceedings of the 29th European Conference on Information Systems, Marrakesh, Morocco (virtual).","DOI":"10.20378\/irb-49912"},{"issue":"3","key":"603_CR71","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1080\/12460125.2020.1798591","volume":"29","author":"RT Watson","year":"2020","unstructured":"Watson, R. T., & Webster, J. (2020). Analyzing the past to prepare for the future: Writing a literature review a roadmap for release 2.0. Journal of Decision Systems, 29(3), 129\u2013147. https:\/\/doi.org\/10.1080\/12460125.2020.1798591","journal-title":"Journal of Decision Systems"},{"issue":"2","key":"603_CR72","first-page":"xiii\u2013xxiii","volume":"26","author":"J Webster","year":"2002","unstructured":"Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 26(2), xiii\u2013xxiii.","journal-title":"MIS Quarterly"},{"key":"603_CR73","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s12525-019-00386-3","volume":"30","author":"J Weking","year":"2020","unstructured":"Weking, J., Mandalenakis, M., Hein, A., Hermes, S., B\u00f6hm, M., & Krcmar, H. (2020). The impact of blockchain technology on business models \u2013 A taxonomy and archetypal patterns. Electronic Markets, 30(2), 285\u2013305. https:\/\/doi.org\/10.1007\/s12525-019-00386-3","journal-title":"Electron Markets"},{"key":"603_CR74","doi-asserted-by":"crossref","unstructured":"Xie, J., Chai, Y., & Liu, X. (2022). An interpretable deep learning approach to understand health. Misinformation transmission on YouTube. Proceedings of the Hawaii 55th International Conference on System Sciences, Maui, USA (virtual).","DOI":"10.24251\/HICSS.2022.183"},{"key":"603_CR75","doi-asserted-by":"publisher","unstructured":"Zhang, K., Xu, P., Gao, T., & Zhang, J. (2021). A trustworthy framework of artificial intelligence for power grid dispatching systems. Proceedings of the IEEE International Conference on Digital Twins and Parallel Intelligence, Beijing, China. https:\/\/doi.org\/10.1109\/DTPI52967.2021.9540198","DOI":"10.1109\/DTPI52967.2021.9540198"},{"key":"603_CR76","unstructured":"Zschech, P., Weinzierl, S., Hambauer, N., Zilker, S., & Kraus, M. (2022). GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints. Proceedings of the 30th European Conference on Information Systems, Timisoara, Romania."},{"key":"603_CR77","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-87617-2","volume-title":"New methods of thought and procedure","author":"F Zwicky","year":"1967","unstructured":"Zwicky, F. (1967). The morphological approach to discovery, invention, research and construction. In F. Zwicky & A. G. Wilson (Eds.), New methods of thought and procedure. Springer."}],"container-title":["Electronic Markets"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12525-022-00603-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12525-022-00603-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12525-022-00603-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T17:52:10Z","timestamp":1701453130000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12525-022-00603-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,23]]},"references-count":77,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["603"],"URL":"https:\/\/doi.org\/10.1007\/s12525-022-00603-6","relation":{},"ISSN":["1019-6781","1422-8890"],"issn-type":[{"value":"1019-6781","type":"print"},{"value":"1422-8890","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,23]]},"assertion":[{"value":"31 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}