AI and its Opportunities for Decision-Making in Organizations: A Systematic Review of the Influencing Factors on the Intention to use AI

Table of contents

Bibliographic information


Cover of Volume: Die Unternehmung Volume 75 (2021), Issue 3
Full access

Die Unternehmung

Volume 75 (2021), Issue 3


Authors:
Publisher
Nomos, Baden-Baden
Copyright Year
2021
ISSN-Online
0042-059X
ISSN-Print
0042-059X

Chapter information


Full access

Volume 75 (2021), Issue 3

AI and its Opportunities for Decision-Making in Organizations: A Systematic Review of the Influencing Factors on the Intention to use AI


Authors:
ISSN-Print
0042-059X
ISSN-Online
0042-059X


Preview:

One domain of application of artificial intelligence (AI) is decision support, particularly in management. Although there are already research streams examining the interaction of AI and humans (e.g. the stream on "hybrid intelligence"), there are still numerous open research gaps - for example, a comprehensive overview of which factors favor the intention to use AI is missing. By conducting a systematic literature review, we identify the factors that potentially positively influence AI usage intentions for decision-making processes in organizations. From this, we create a framework that both provides practical implications for the successful use of AI in organizational decision-making processes and delivers further research approaches, for example, on the validity/ usability of proven IS adoption models in the present context.

Bibliography


  1. Balakrishnan, Tara; Chui, Michael; Hall, Bryce; Henke, Nicolaus (2020): The State of AI in 2020. Edited by McKinsey & Company. Available online at https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/global-survey-the-state-of-ai-in-2020, checked on 4/29/2021. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-319
  2. Candelon, François; Reichert, Tom; Duranton, Sylvain; Rodolphe, Charme di Carlo; Stokol, Georgie (2020): Deploying AI to Maximize Revenue. Edited by BCG Henderson Institute. Available online at https://www.bcg.com/de-de/publications/2020/deploying-ai-artificial-intelligence-to-maximize-revenue, checked on 4/29/2021. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-319
  3. Chen, Hsinchun; Chiang, Roger H. L.; Storey, Veda C. (2012): Business Intelligence and Analytics: From Big Data to Big Impact. In MIS Quarterly 36 (4), pp. 1165–1188. DOI: 10.2307/41703503. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-319
  4. Grover, Varun; Chiang, Roger H.L.; Liang, Ting-Peng; Zhang, Dongsong (2018): Creating Strategic Business Value from Big Data Analytics: A Research Framework. In Journal of Management Infor-mation Systems 35 (2), pp. 388–423. DOI: 10.1080/07421222.2018.1451951. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-319
  5. International Data Group (2021): IDC Forecasts Improved Growth for Global AI Market in 2021. Available online at https://www.idc.com/getdoc.jsp?containerId=prUS47482321, checked on 4/29/2021. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-319
  6. Kraus, Mathias; Feuerriegel, Stefan; Oztekin, Asil (2020): Deep Learning in Business Analytics and Operations Research: Models, Applications and Managerial Implications. In European Journal of Operational Research 281 (3), pp. 628–641. DOI: 10.1016/j.ejor.2019.09.018. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-319
  7. Raisch, Sebastian; Krakowski, Sebastian (2020): Artificial Intelligence and Management: The Auto-mation-Augmentation Paradox. In Academy of Management Review. DOI: 10.5465/2018.0072. (forthcoming) Open Google Scholar DOI: 10.5771/0042-059X-2021-3-319
  8. Schilling, Melissa A. (2015): Technology Shocks, Technological Collaboration, and Innovation Out-comes. In Organization Science 26 (3), pp. 668–686. DOI: 10.1287/orsc.2015.0970. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-319
  9. Wu, Lynn; Hitt, Lorin; Lou, Bowen (2020): Data Analytics, Innovation, and Firm Productivity. In Man-agement Science 66 (5), pp. 2017–2039. DOI: 10.1287/mnsc.2018.3281. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-319
  10. Wu, Lynn; Lou, Bowen; Hitt, Lorin (2019): Data Analytics Supports Decentralized Innovation. In Ma-nagement Science 65 (10), pp. 4863–4877. DOI: 10.1287/mnsc.2019.3344. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-319
  11. Agrawal, A. et al. (2017): What to Expect from Artificial Intelligence, in: MIT Sloan Management Review, Vol. 28, No. 3, pp. 23-27. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  12. Baden-Fuller, C./Haefliger, S. (2013): Business Models and Technological Innovation, in: Long Range Planning, Vol. 46, pp. 419–426. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  13. Baryannis, G. et al. (2019): Supply Chain Risk Management and Artificial Intelligence: State of the Art and Future Research Directions, in: International Journal of Production Research, Vol. 57, No. 7, pp. 2179–2202. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  14. Brock, J./von Wangenheim, F. (2019): Demystifying AI: What Digital Transformation Leaders Can Teach You About Realistic Artificial Intelligence, in: California Management Review, Vol. 61, No. 4, pp. 110–134. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  15. Burmeister, C. et al. (2016): Business Model Innovation for Industrie 4.0: Why the "Industrial Internet" Mandates a New Perspective on Innovation, in: Die Unternehmung, Vol. 70, No. 2, pp. 124–152. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  16. Chesbrough, H. (2007): Business Model Innovation: It's Not Just About Technology Anymore, in: Strategy & Leadership, Vol. 35, No. 6, pp. 12–17. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  17. Ciampi, F. et al. (2021): Exploring the Impact of Big Data Analytics Capabilities on Business Model Innovation: The Mediating Role of Entrepreneurial Orientation, in: Journal of Business Research, Vol. 123, pp. 1–13. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  18. Davenport, T./Ronanki, R. (2018): Artificial Intelligence for the Real World, in: Harvard Business Review, Vol. 96, No. 1, pp. 108–116. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  19. Eisenhardt, K. (1989): Building Theories from Case Study Research, in: The Academy of Management Review, Vol. 14, No. 4, pp. 532–550. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  20. Fjeldstad, Ø./Snow, C. (2018): Business Models and Organization Design, in: Long Range Planning, Vol. 51, No. 1, pp. 32–39. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  21. Foss, N. J./Saebi, T. (2017): Fifteen Years of Research on Business Model Innovation: How Far Have We Come, and Where Should We Go?, in: Journal of Management, Vol. 43, No. 1, pp. 200–227. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  22. Hamel, G. (2000): Leading the Revolution. Harvard Business School Press. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  23. Herriott, R./Firestone, W. (1983): Multisite Qualitative Policy Research: Optimizing Description and Generalizability, in: Educational Researcher, Vol. 12, pp. 14–19. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  24. Hildebrandt, B. et al. (2015): Entering the Digital Era – the Impact of Digital Technology-Related M&As on Business Model Innovations of Automobile OEMs, in: ICIS 2015 Proceedings. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  25. Hitt, L. M./Brynjolfsson, E. (1996): Productivity, Business Profitability, and Consumer Surplus: Three Different Measures of Information Technology Value, in: MIS Quarterly, Vol. 20, No. 2, pp. 121–142. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  26. Khamparia, A./Singh, K. M. (2019): A Systematic Review on Deep Learning Architectures and Applications, in: Expert Systems, Vol. 36, No. 3, pp. 1–22. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  27. Kshetri, N. (2020): Artificial Intelligence in Human Resource Management in the Global South, in: AMCIS 2020 Proceedings. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  28. Lee, A./Baskerville, R. (2003): Generalizing Generalizability in Information Systems Research, in: Information Systems Research, Vol. 14, No. 3, pp. 221–243. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  29. Lee, J. et al. (2019): Emerging Technology and Business Model Innovation: The Case of Artificial Intelligence, in: Journal of Open Innovation: Technology, Market, and Complexity, Vol. 5, No. 3, pp. 1–13. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  30. Levkovskyi, B. et al. (2020): Why Do Organizations Change? A Literature Review on Drivers and Measures of Success for Digital Transformation, in: AMCIS 2020 Proceedings. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  31. Lokuge, S. et al. (2020): The Next Wave of CRM Innovation: Implications for Research, Teaching, and Practice, in: Communications of the AIS, Vol. 46, No. 1, pp. 23–46. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  32. Markides, C./Charitou, C. D. (2004): Competing with Dual Business Models – a Contingency Approach, in: Academy of Management Perspectives, Vol. 18, No. 3, pp. 22–36. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  33. May, A. et al. (2020): Realizing Digital Innovation from Artificial Intelligence, in: ICIS 2020 Proceedings. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  34. Metzler, D. R./Muntermann, J. (2020): The Impact of Digital Transformation on Incumbent Firms: An Analysis of Changes, Challenges, and Responses at the Business Model Level, in: ICIS 2020 Proceedings. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  35. Myers, M. D. (2009): Qualitative Research in Business & Management. SAGE Publications. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  36. Neuhüttler, J. et al. (2020): Artificial Intelligence as Driver for Business Innovation in Smart Service Systems, in: AHFE 2020 Proceedings. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  37. Osmundsen, K./Bygstad, B. (2020): Patterns of Interaction: Making Sense of Digitalization in Incumbent Firms, in: SCIS 2020 Proceedings. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  38. Osterwalder, A./Pigneur, Y. (2010): Business Model Generation – a Handbook for Visionaries, Game Changers, and Challengers. John Wiley and Sons. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  39. Plastino, E./Purdy, M. (2018): Game Changing Value from Artificial Intelligence: Eight Strategies, in: Strategy & Leadership, Vol. 46, No. 1, pp. 16–22. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  40. Russell, S./Norvig, P. (2003): Artificial Intelligence: A Modern Approach, 2nd Ed. Prentice Hall. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  41. Sebastian, I. et al. (2017): How Big Old Companies Navigate Digital Transformation, in: MIS Quarterly Executive, Vol. 16, No. 3, pp. 201–213. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  42. Sharda, R. et al. (2021): Analytics, Data Science, and Artificial Intelligence: Systems for Decision Support, 11th Ed. Pearson Education. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  43. Sorescu, A. (2017): Data-Driven Business Model Innovation, in: Journal of Product Innovation Management, Vol. 34, No. 5, pp. 691–696. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  44. Soto Setzke, D. et al. (2020): Pathways to Successful Business Model Innovation in the Context of Digital Transformation, in: PACIS 2020 Proceedings. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  45. Stormi, K. et al. (2018): Feasibility of B2C Customer Relationship Analytics in the B2B Industrial Context, in: ECIS 2018 Proceedings. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  46. Svahn, F. et al. (2017): Embracing Digital Innovation in Incumbent Firms: How Volvo Cars Managed Competing Concerns, in: MIS Quarterly, Vol. 41, No. 1, pp. 239–253. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  47. Veit, D. et al. (2014): Business Models: An Information Systems Research Agenda, in: Business & Information Systems Engineering, Vol. 6, No. 1, pp. 45–53. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  48. Westerman, G./Bonnet, D. (2015): Revamping Your Business through Digital Transformation, in: MIT Sloan Management Review, Vol. 56, No. 3, pp. 10–15. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  49. Wirtz, B. et al. (2016): Business Models: Origin, Development and Future Research Perspectives, in: Long Range Planning, Vol. 49, No. 1, pp. 36–54. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  50. Yin, R. (2014): Case Study Research, 5th Ed. SAGE Publications. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  51. Zott, C. et al. (2011): The Business Model: Recent Developments and Future Research, in: Journal of Management, Vol. 37, No. 4, pp. 1019–1042. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-324
  52. Amershi S., et al. (2019): Software Engineering for Machine Learning: A Case Study, at: 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) (pp. 291–300), IEEE. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  53. Brynjolfsson, E./McAfee, A. (2017): The business of artificial intelligence, in: Harvard Business Review, 7, 3–11, URL: https://starlab-alliance.com/wp-content/uploads/2017/09/The-Business-of-Artificial-Intelligence.pdf, retrieved: March 11, 2021. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  54. Brynjolfsson, E./Mitchell, T. (2017): What can machine learning do? Workforce implications, in: Science, 358 (6370), 1530–1534. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  55. Canon Business Process Services (2019): Five Steps to Reducing Your Paper Processing Costs, URL: https://cbps.canon.com/insights/five-steps-to-reducing-your-paper-processing-costs, retrieved: December 22, 2020. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  56. Chen, H.-C., et al. (2012): Business Intelligence and Analytics: From Big Data to Big Impacts, in: MIS Quarterly, 36 (4), 1165–1188. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  57. Chui, M./Malhotra, S. (2018): AI adoption advances, but foundational barriers remain, URL: https://www.mckinsey.com/featured-insights/artificial-intelligence/ai-adoption-advances-but-foundational-barriers-remain, retrieved: March 11, 2021. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  58. Clark, M. (2020): Business Impact of Intelligent Document Processing (IDP), URL: https://www.infrrd.ai/blog/business-impact-of-intelligent-document-processing, retrieved: March 16, 2021. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  59. Corbin, J./Strauss, A. (2014): Basics of qualitative research: Techniques and procedures for developing grounded theory. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  60. Côrte-Real, N., et al. (2017): Assessing business value of Big Data Analytics in European firms, in: Journal of Business Research, 70, 379–390. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  61. Denk, T. I./Reisswig, C. (2019): BERTgrid: Contextualized Embedding for 2D Document Representation and Understanding, at: "Document Intelligence" workshop of 33rd NeurIPS 2019. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  62. Garncarek, Ł., et al. (2020): LAMBERT: Layout-Aware (Language) Modeling using BERT for information extraction, URL: https://arxiv.org/abs/2002.08087, retrieved: March 19, 2021. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  63. Hyland Software Inc. (2019): 10 Things You Need To Know About Intelligent Capture, URL: https://www.hyland.com/-/media/Files/hyland/ebooks/brainware-ebook-ten-things-intelligent-data-capture.pdf?la=en, retrieved: December 15, 2020. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  64. Jyoti, R., et al. (2019): IDC FutureScape: Worldwide Artificial Intelligence (AI) 2020 Predictions, IDC Corporate USA, URL: https://www.idc.com/getdoc.jsp?containerId=US45576319, retrieved: March 17, 2021. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  65. Katti, A. R., et al. (2018): Chargrid: Towards Understanding 2D Documents, at: EMNLP 2018. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  66. Koch, B. (2019): The e-invoicing journey 2019–2025, URL: https://compacer.com/wp-content/uploads/2019/05/Billentis_Report_compacer_single-sponsor.pdf, retrieved: March 16, 2021. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  67. Lample, G., et al. (2016): Neural Architectures for Named Entity Recognition, in: Proceedings of NAACL 2016. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  68. Levvel Research (2019): Pitching ROI for Accounts Payable Automation, URL: Levvel Research: https://cdn.levvel.io/reports/PitchingRIO_Report_Rebrand.pdf, retrieved: November 28, 2020. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  69. Liu, X., et al. (2019): Graph Convolution for Multimodal Information Extraction from Visually Rich Documents, at: NAACL 2019. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  70. Lorica, B./Nathan, P. (2019): AI Adoption in the Enterprise. How Companies Are Planning and Prioritizing AI Projects in Practice. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  71. Mansfield, E., et al. (1977): Social and Private Rates of Return From Industrial Innovation, in: Quarterly Journal of Economics, 91 (2), 221–240. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  72. Manyika, J., et al. (2017): A future that works: automation, employment, and productivity, URL: https://www.mckinsey.com/~/media/mckinsey/featured%20insights/Digital%20Disruption/Harnessing%20automation%20for%20a%20future%20that%20works/MGI-A-future-that-works-Full-report.ashx, retrieved: March 11, 2021. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  73. McAfee, A./Brynjolfsson, E. (2012): Big Data: The Management Revolution, URL: https://hbr.org/2012/10/big-data-the-management-revolution, retrieved March: 16, 2021. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  74. Mizik, N./Jacobsen, R. (2003): Trading Off Between Value Creation and Value Appropriation: The Financial Implications of Shifts in Strategic Emphasis, in: Journal of Marketing, 67 (1), 63–76. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  75. Ng, A. (2016): What AI Can and Can’t Do, from https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now, retrieved: March 16, 2021. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  76. Osterwalder, A. (2004): The business model ontology: A proposition in a design science approach, URL: http://www.hec.unil.ch/aosterwa/PhD/Osterwalder_PhD_BM_Ontology.pdf, retrieved: August 21, 2020. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  77. Osterwalder, A./Pigneur, Y. (2010): Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  78. Palm, R. B., et al. (2017): CloudScan – A configuration-free invoice analysis system using recurrent neural networks, ICDAR 2017. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  79. Parikh, A. P., et al. (2016): A Decomposable Attention Model for Natural Language Inference, in: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  80. Parisi, G. I., et al. (2019): Continual Lifelong Learning with Neural Networks: A Review, URL: https://arxiv.org/abs/1802.07569, retrieved March: 17, 2021. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  81. Prahalad, C. K./Hamel, G. (1990): The Core Competence of the Corporation, in: Harvard Business Review, 68 (3), 79–91, URL: https://hbr.org/1990/05/the-core-competence-of-the-corporation, retrieved: March 16, 2021. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  82. Prasad, S. (2020): How is Intelligent Document processing disrupting traditional workflows?, URL: https://www.klearstack.com/intelligent-document-processing-technology/, retrieved: November 29, 2020. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  83. Rai, A. (2020): Explainable AI: from black box to glass box, in: Journal of the Academy of Marketing Science, 48, 137–141. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  84. Reis, C., et al. (2020): Assessing the drivers of machine learning business value, in: Journal of Business Research, 117, 232–243. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  85. Reisswig, C., et al. (2019): Chargrid-OCR: End-to-end trainable Optical Character Recognition through Semantic Segmentation and Object Detection, at: Neur IPS 2019 Workshop Document Intelligence. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  86. Sanders, N. (2016): How to Use Big Data to Drive Your Supply Chain, in: California Management Review, 26–48. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  87. Schilling, M. A. (2015): Technology shocks, technological collaboration, and innovation outcomes, in: Organizational Science, 26 (3), 668–686. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  88. Strauss, A. (1987): Qualitative Analysis for Social Scientists, Cambridge University Press. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  89. Wei, M., et al. (2020): Robust Layout-aware IE for Visually Rich Documents with Pre-trained Language Models, in: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2367–2376, URL: https://arxiv.org/abs/2005.11017, retrieved: March 17, 2021. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  90. Xu, Y., et al. (2019): LayoutLM: Pre-training of Text and Layout for Document Image Understanding, in: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1192–1200, URL: https://arxiv.org/abs/1912.13318, retrieved: March 17, 2021. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  91. Yu, W., et al. (2020): PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks, at: International Conference on Pattern Recognition 2020, URL: https://arxiv.org/abs/2004.07464, retrieved: March 17, 2021. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  92. Zhang P., et al. (2020): TRIE: End-to-End Text Reading and Information Extraction for Document Understanding, in: Proceedings of the 28th ACM International Conference on Multimedia, 1413–1422, URL: https://arxiv.org/abs/2005.13118, retrieved: March 17, 2021. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-340
  93. Bellman, A. (1978): Artificial Intelligence: Can Computers Think? Thomson Course Technology. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  94. Charniak, E./McDermott, D. (1985): Introduction to Artificial Intelligence. Reading, MA: Addision-Wesley. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  95. Chui, M. et al. (2018): An executive’s guide to AI. Verfügbar unter https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/an-executives-guide-to-ai (Zugriff am 21.08.2020). Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  96. Colson, E. (2019): What AI-Driven Decision Making Looks Like. Harvard Business Review. Verfügbar unter https://hbr.org/2019/07/what-ai-driven-decision-making-looks-like (Zugriff am 14.08.2020). Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  97. Davenport, H./Kirby, J. (2016): Just how smart are smart machines?. MIT Sloan Management Review, 57(3), S. 21. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  98. Davenport, T. et al. (2020): How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), S. 24–42. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  99. Gentsch, P. (2019): Künstliche Intelligenz für Sales, Marketing und Service. New York, NY: Springer Publishing. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  100. Gioia, A. et al. (2013): Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational research methods, 16(1), S. 15–31. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  101. Glaser, B./Strauss, L. (1967): The Discovery of Grounded Theory. Chicago: Aldine. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  102. Huang, H./Rust, T. (2018): Artificial intelligence in service. Journal of Service Research, 21(2), S. 155–172. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  103. Jaeger, U./Reinecke, S. (2009): Expertengespräch. In: Baumgarth, C. et al. (Hrsg.): Empirische Mastertechniken: eine anwendungsorientierte Einführung für die Marketing- und Managementforschung. Wiesbaden: Gabler, S. 29–76. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  104. Kaplan, A./Haenlein, M. (2019): Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), S. 15–25. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  105. Kietzmann, J. et al. (2018): Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. Journal of Advertising Research, 58(3), S. 263–267. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  106. Kolbjørnsrud, V. et al. (2016): How artificial intelligence will redefine management. Harvard Business Review, 2. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  107. Kurzweil, R. (1990): The age of intelligent machines. MIT Press Cambridge, MA, USA. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  108. Liebold, R./Trinczek, R. (2009): Experteninterview. In: Kühl, S. et al. (Hrsg.): Handbuch der Organisationsforschung. Quantitative und Qualitative Methoden. Wiesbaden, S. 32–56. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  109. McCarthy, J. et al. (1955): A proposal for the Dartmouth Summer Research Project on Artificial Intelligence. Research Project on Artificial Intelligence. Verfügbar unter http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html (Zugriff am 20.07.2020). Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  110. McCarthy, J. (2007): What is artificial intelligence? Basic Questions. Computer Science Department, Stanford University. Verfügbar unter https://stanford. io/2lSo373 (Zugriff am 20.07.2020). Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  111. Morse, M., et al. (2002): Verification strategies for establishing reliability and validity in qualitative research. International journal of qualitative methods, 1(2), S. 13–22. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  112. Nilsson, J. (1998): Artificial Intelligence: A New Synthesis. Morgan Kaufmann Publishers. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  113. Palinkas, A. et al. (2015): Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and policy in mental health and mental health services research, 42(5), S. 533–544. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  114. Rich, E./Knight, K. (1991): Artificial Intelligence. Artificial Intelligence Series. 2. Auflage. McGraw-Hill. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  115. Russell, S./Norvig, P. (2010): Artificial intelligence: A modern approach. Upper Saddle River: Prentice-Hall. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  116. Rust, T. (2020): The future of marketing. International Journal of Research in Marketing, 37(1), S. 15–26. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  117. Torra, V. et al. (2019): Artificial intelligence, In: Said, A./Torra, V. (Hrsg.): Data science in practice. Springer, S. 9–26. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  118. Traumer, F. et al. (2017): Towards a Future Reallocation of Work between Humans and Machines – Taxonomy of Tasks and Interaction Types in the Context of Machine Learning. International Conference on Information Systems (ICIS). Seoul, South Korea. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  119. Wassermann, S. (2015): Das qualitative Experteninterview. In: Niederberger, M./Wassermann, S. (Hrsg.): Methoden der Experten-und Stakeholdereinbindung in der sozialwissenschaftlichen Forschung, Springer VS, Wiesbaden, S. 51–67. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  120. Wilson, J./Daugherty, R. (2018): Collaborative intelligence: humans and AI are joining forces. Harvard Business Review, 96(4), S. 114–123. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  121. Wolan, M. (2020): Künstliche Intelligenz verändert alles. In: Wolan, M. (Hrsg.): Next Generation Digital Transformation. Springer Gabler, Wiesbaden, S. 25–50. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-359
  122. Bauer, E./Kohavi, R. (1999): An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants, in: Machine Learning, Vol. 36, No. 1, S. 105-139. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  123. Breiman, L. (1996): Bagging Predictors, in: Machine Learning, Vol. 24, No. 2, S. 123–140. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  124. Breiman, L. (2001): Random Forests, in: Machine Learning, Vol. 45, No. 1, S. 5–32. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  125. Breiman, L./Cutler, A. (2004): Random Forests. www.stat.berkeley.edu/~breiman/RandomForests/ (accessed 2020/06/07). Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  126. Breiman, L./Friedman, J. H./Stone, C. J./Olshen, R. A. (1984): Classification and Regression Trees. Belmont, CA. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  127. Bughin, J./Seong, J./Manyika, J./Chui, M./Joshi, R. (2018): Notes from the AI Frontier: Modeling the Impact of AI on the World Economy. www.mckinsey.com (accessed 2020/01/02). Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  128. Chen, T./Guestrin, C. (2016): XGBoost: A Scalable Tree Boosting System, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA, Association for Computing Machinery, S. 785–794. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  129. Chen, T./He, T./Benesty, M./Khotilovich, V./Tang, Y. (2020): xgboost: eXtreme Gradient Boosting, in: R Package Version 0.4 – 2. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  130. De Bock, K. W./Poel, D. V. d. (2011): An Empirical Evaluation of Rotation-Based Ensemble Classifiers for Customer Churn Prediction, in: Expert Systems with Applications, Vol. 38, No. 10, S. 12293–12301. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  131. Freund, Y./Schapire, R. E. (1997): A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, in: Journal of Computer and System Sciences, Vol. 55, No. 1, S. 119–139. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  132. Friedman, J. H. (2001): Greedy Function Approximation: A Gradient Boosting Machine, in: Annals of Statistics, Vol. 29, No. 5, S. 1189–1232. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  133. Gensler, S./Leeflang, P./Skiera, B. (2012): Impact of Online Channel Use on Customer Revenues and Costs to Serve: Considering Product Portfolios and Self-Selection, in: International Journal of Research in Marketing, Vol. 29, No. 2, S. 192–201. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  134. Gini, C. (1912): Variabilità e Mutabilità. Contributo allo Studio delle Distribuzioni e delle Relazioni Statistiche. Bologna. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  135. Goldstein, A./Kapelner, A./Bleich, J./Pitkin, E. (2015): Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation, in: Journal of Computational and Graphical Statistics, Vol. 24, No. 1, S. 44–65. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  136. Ha, K./Cho, S./MacLachlan, D. (2005): Response Models Based on Bagging Neural Networks, in: Journal of Interactive Marketing, Vol. 19, No. 1, S. 17–30. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  137. Kamakura, W. A./Ramaswami, S. N./Srivastava, R. K. (1991): Applying Latent Trait Analysis in the Evaluation of Prospects for Cross-Selling of Financial Services, in: International Journal of Research in Marketing, Vol. 8, No. 4, S. 329–349. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  138. Kim, Y./Street, W. N. (2004): An Intelligent System for Customer Targeting: a Data Mining Approach, in: Decision Support Systems, Vol. 37, No. 2, S. 215–228. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  139. Knott, A./Hayes, A./Neslin, S. A. (2002): Next-Product-to-Buy Models for Cross-Selling Applications, in: Journal of Interactive Marketing, Vol. 16, No. 3, S. 59–75. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  140. Kumar, V./George, M./Pancras, J. (2008): Cross-Buying in Retailing: Drivers and Consequences, in: Journal of Retailing, Vol. 84, No. 1, S. 15–27. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  141. Larivière, B./Van den Poel, D. (2005): Predicting Customer Retention and Profitability by Using Random Forests and Regression Forests Techniques, in: Expert Systems with Applications, Vol. 29, No. 2, S. 472–484. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  142. Lemmens, A./Gupta, S. (2020): Managing Churn to Maximize Profits, in: Marketing Science, Vol. 39, No. 5, S. 956–973. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  143. Lessmann, S./Haupt, J./Coussement, K./De Bock, K. W. (2021): Targeting Customers for Profit: An Ensemble Learning Framework to Support Marketing Decision-Making, in: Information Sciences, Vol. 557, No. 2021, S. 286–301. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  144. Li, S./Sun, B./Wilcox, R. T. (2005): Cross-Selling Sequentially Ordered Products: An Application to Consumer Banking Services, in: Journal of Marketing Research, Vol. 42, No. 2, S. 233–239. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  145. Liaw, A./Wiener, M. (2002): Classification and Regression by randomForest, in: R News, Vol. 2, No. 3, S. 18–22. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  146. Lundberg, S. M./Lee, S.-I. (2017): A Unified Approach to Interpreting Model Predictions, in: Proceedings of the 30th Conference on Advances in Neural Information Processing Systems, S. 4765–4774. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  147. Mende, M./Bolton, R. N./Bitner, M. J. (2013): Decoding Customer–Firm Relationships: How Attachment Styles Help Explain Customers' Preferences for Closeness, Repurchase Intentions, and Changes in Relationship Breadth, in: Journal of Marketing Research, Vol. 50, No. 1, S. 125–142. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  148. Prinzie, A./Van den Poel, D. (2008): Random Forests for Multiclass Classification: Random MultiNomial Logit, in: Expert Systems with Applications, Vol. 34, No. 3, S. 1721–1732. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  149. Reinartz, W./Kumar, V. (2002): The Mismanagement of Customer Loyalty, in: Harvard Business Review, Vol. 80, No. 7, S. 86–94, 125. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  150. Ribeiro, M. T./Singh, S./Guestrin, C. (2016): Why Should I Trust You? Explaining the Predictions of Any Classifier, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, S. 1135–1144. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  151. Schwarz, B., [@xaprb]. (2019, February 19). When you’re fundraising, it’s AI. Twitter. https://twitter.com/xaprb. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  152. Shmueli, G./Koppius, O. R. (2011): Predictive Analytics in Information Systems Research, in: MIS Quarterly, Vol. 35, No. 3, S. 553–572. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  153. Steinhoff, L./Arli, D./Weaven, S./Kozlenkova, I. V. (2019): Online Relationship Marketing, in: Journal of the Academy of Marketing Science, Vol. 47, No. 3, S. 369–393. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  154. Therneau, T./Atkinson, B./Ripley, B. (2019): rpart: Recursive Partitioning and Regression Trees, in: R Package Version 4.1 – 15. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  155. Verhoef, P. C./Franses, P. H./Hoekstra, J. C. (2001): The Impact of Satisfaction and Payment Equity on Cross-Buying: A Dynamic Model for a Multi-Service Provider, in: Journal of Retailing, Vol. 77, No. 3, S. 359–378. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-376
  156. Barber, B. M.; Odean, T. (2001): Boys will be boys: Gender, overconfidence, and common stock investment. In Quarterly Journal of Economics 116 (1), pp. 261–292. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  157. Beketov, M.; Lehmann, K.; Wittke, M. (2018): Robo advisors: quantitative methods inside the robots. In Journal of Asset Management 19 (6), pp. 363–370. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  158. Berk, R. A. (2020): Statistical learning from a regression perspective. 3rd ed. 2020. Cham: Springer International Publishing; Imprint: Springer (Springer Texts in Statistics). Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  159. Breiman, L. (1996): Bagging predictors. In Machine Learning 24 (2), pp. 123–140. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  160. Breiman, L. (2001): Random forests. In Machine Learning 45 (1), pp. 5–32. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  161. Breiman, L.; Friedman, J.; Stone, C. J.; Olshen, R. A. (1984): Classification and regression trees. Boca Raton: CRC Press. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  162. Bruckes, M.; Westmattelmann, D.; Oldeweme, A.; Schewe, G. (2019): Determinants and barriers of adopting robo-advisory services. In Proceedings of the Fortieth International Conference on Information Systems, pp. 1–10. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  163. Bucher-Koenen, T.; Lusardi, A. (2011): Financial literacy and retirement planning in Germany. In Journal of Pension Economics and Finance 10 (4), pp. 565–584. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  164. Calcagno, R.; Monticone, C. (2015): Financial literacy and the demand for financial advice. In Journal of Banking & Finance 50, pp. 363–380. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  165. D’Acunto, F.; Prabhala, N.; Rossi, A. G. (2019): The promises and pitfalls of robo-advising. In Review of Financial Studies 32 (5), pp. 1983–2020. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  166. Dietterich, T. G. (2000): An experimental comparison of three methods for constructing ensem-bles of decision trees: Bagging, boosting, and randomization. In Machine Learning 40 (2), pp. 139–157.eng Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  167. Eurostat (2020): Bevölkerung: Strukturindikation. http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=demo_pjanind&lang=de, checked on 12/29/2020. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  168. Faloon, M.; Scherer, B. (2017): Individualization of robo-advice. In Journal of Wealth Manage-ment 20 (1), pp. 30–36. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  169. Fan, L.; Chatterjee, S. (2020): The utilization of robo-advisors by individual investors: An analysis using diffusion of innovation and information search frameworks. In Journal of Financial Coun-seling and Planning 31 (1), pp. 130–145. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  170. Frank, E.; Bouckaert, R. R. (2009): Conditional density estimation with class probability estima-tors. Zhi-Hua Zhou, Takahi Washio (Eds.): Advances in machine learning. Berlin: Springer, pp. 65–81.eng Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  171. Fulk, M.; Grable, J. E.; Watkins, K.; Kruger, M. (2018): Who uses robo-advisory services, and who does not? In Financial Services Review 27, pp. 173–188. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  172. Gentile, M.; Soccorso, P. (2016): Financial advice seeking, financial knowledge and overconfi-dence. Evidence from the Italian market. In CONSOB Discussion Papers (83), pp. 1–45. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  173. Gibson, R.; Michaylik, D.; van de Venter, G. (2013): Financial risk tolerance: An analysis of unex-plored factors. In Financial Services Review 22, pp. 23–50. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  174. Gregorutti, B.; Michel, B.; Saint-Pierre, P. (2017): Correlation and variable importance in random forests. In Stat Comput 27 (3), pp. 659–678. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  175. Hackethal, A.; Haliassos, M.; Jappelli, T. (2012): Financial advisors: A case of babysitters? In Journal of Banking & Finance 36 (2), pp. 509–524. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  176. Hohenberger, C.; Lee, C.; Coughlin, J. F. (2019): Acceptance of robo‐advisors: Effects of finan-cial experience, affective reactions, and self‐enhancement motives. In Financial Planning Review 2 (2), pp. 1–14. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  177. Joo, S.; Grable, J. E. (2001): Factors associated with seeking and using professional retirement-planning help. In Family and Consumer Sciences Research Journal 30 (1), pp. 37–63. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  178. Jung, D.; Erdfelder, E.; Glaser, F. (2018): Nudged to win: Designing robo-advisory to overcome decision inertia. In Proceedings of the 26th European Conference on Information Systems (ECIS 2018). https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1018&context=ecis2018_rip, checked on 2/18/2020. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  179. McKinsey (2015): The fountain of growth – demographics and wealth management. https://www.mckinsey.com/~/media/McKinsey/Industries/Financial%20Services/PriceMetrix/Our%20Insights/The%20Fountain%20of%20Growth/The-Fountain-of-Growth-Screen-Final.pdf, checked on 1/4/2021. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  180. Menardi, G.; Torelli, N. (2014): Training and assessing classification rules with imbalanced data. In Data Mining and Knowledge Discovery 28 (1), pp. 92–122. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  181. Merkle, C. (2020): Robo-advice and the future of delegated investment. In Journal of Financial Transformation 51, pp. 20–27. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  182. Niehues, J.; Stockhausen, M. (2020): Einkommensverteilung in Deutschland: Wer zur Oberschicht gehört. https://www.iwkoeln.de/presse/pressemitteilungen/beitrag/judith-niehues-maximilian-stockhausen-wer-zur-oberschicht-gehoert.html, checked on 8/17/2020. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  183. Palczewska, A.; Palczewski, J.; Robinson, R. Marchese; Neagu, D. (2013): Interpreting random forest models using a feature contribution method. In 2013 IEEE 14th International Conference Proceedings, pp. 112–119. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  184. Robb, C.; Babiarz, P.; Woodyard, A. S. (2012): The demand for financial professionals' advice: The role of financial knowledge, satisfaction, and confidence. In Financial Services Review 21 (4), pp. 291–305. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  185. Ruf, C.; Back, A.; Bergmann, R.; Schlegel, M. (2015): Elicitation of requirements for the design of mobile financial advisory services – Instantiation and validation of the requirement data model with a multi-method approach. In 48th Hawaii International Conference on System Sciences. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  186. Siroky, D. S.; Banks, D.; Bustikova, L.; Lunagomez, S.; Wegman, E. (2009): Navigating random forests and related advances in algorithmic modeling. In Statistics Surveys 3, pp. 147–163. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  187. Sironi, P. (2016): FinTech innovation: From robo-advisors to goal based investing and gamification. Chichester, West Sussex: Wiley (Wiley finance series). http://onlinelibrary.wiley.com/book/10.1002/9781119227205. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  188. Statistisches Bundesamt (2020): Durchschnittsalter auf Grundlage des Zensus 2011 nach Ge-schlecht und Staatsangehörigkeit. https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bevoelkerung/Bevoelkerungsstand/Tabellen/durchschnittsalter-zensus-jahre.html, checked on 12/29/2020. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  189. Tertilt, M.; Scholz, P. (2018): To advise, or not to advise – How robo-advisors evaluate the risk preferences of private investors. In Journal of Wealth Management 21 (2), pp. 70–84. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  190. Woodyard, A. S.; Grable, J. E. (2018): Insights into the users of robo-advisory firms. In Journal of Financial Service Professionals 5, pp. 56–66. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-397
  191. Adler, T./Kritzman, M. (2007): Mean-variance versus full-scale optimisation: In and out of sample, in: Journal of Asset Management Vol. 7, No. 5, S. 302–311. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  192. Admati, A. (1985): A Noisy Rational Expectations Equilibrium for Multiple Asset Securities Markets, in: Econometrica, Vol. 53, Nr. 3, S. 629–57. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  193. Baker, T./Dellaert, B. (2017): Regulating robo advice across the financial services industry, in: Iowa Law Review, Vol. 103, S. 713–750. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  194. Ban, G. Y./El Karoui, N./Lim, A. E. (2018): Machine learning and portfolio optimization, in: Management Science, Vol. 64, Mo. 3, S. 1136–1154. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  195. Bartov, E./Faurel, L./Mohanram, P. S. (2018): Can Twitter help predict firm-level earnings and stock returns?, in: The Accounting Review, Vol. 93, No. 3, S. 25–57. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  196. Beketov, M./Lehmann, K./Wittke, M. (2018): Robo Advisors – Quantitative Methods inside the Robots, in: Journal of Asset Management, Vol. 19, No. 6, S. 363–370. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  197. Biais, B./Bossaerts, P./Spatt, C. (2010): Equilibrium asset pricing and portfolio choice under asymmetric information, in: The Review of Financial Studies, Vol. 23, Nr. 4, S. 1503–1543. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  198. Black, F./Littermann, R. (1992): Global Portfolio Optimization, in: Financial Analysts Journal, Vol. 48, No. 5, S. 28–43. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  199. Bollerslev, T. (1986): Generalized Autoregressive Conditional Heteroskedasticity, in: Journal of Econometrics, Vol. 31, No. 3, S. 307–327. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  200. Calo, R. (2017): Artificial Intelligence policy: A primer and roadmap, in: UCDL Rev., Vol. 51, 399–427. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  201. Carhart, M. M. (1997): On Persistence in Mutual Fund Performance, in: The Journal of Finance, Vol. 52, No. 1, S. 57–82. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  202. Cremers, J. H./Kritzman, M./Page, S. (2005): Optimal hedge fund allocations, in: Journal of Portfolio Management, Vol. 31, No. 3, S. 70–81. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  203. Dafoe, A. (2018): AI governance: Research Agenda. Governance of AI Program, Future of Humanity Institute, University of Oxford: Oxford, UK. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  204. Daniel, K./Hirshleifer, D./Subrahmanyam, A. (1998): Investor Psychology and Security Market Under- and Overreactions, in: Journal of Finance, Vol. 53, No. 6, S. 1839–1886. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  205. Das, S./Chen, M. (2007): Yahoo! for Amazon: Sentiment extraction from small talk on the web, in: Management Science, Vol. 53, No. 9, S. 1375–1388. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  206. De Bondt, W.F.M./Thaler, R.H. (1995): Financial Decision-Making in Markets and Firms: A Behavioral Perspective, in: Handbooks in Operations Research and Management Science, Vol. 9, S. 385–410. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  207. DeepMind (2016): Google DeepMind: Ground-breaking AlphaGo masters the game of Go, url: https://www.youtube.com/watch?v=SUbqykXVx0A, accessed 21st November 2020. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  208. DEK (2019): Gutachten der Datenethikkommission der Bundesregierung, in: Datenethikkommission (DEK) der Bundesregierung Bundesministerium des Innern, für Bau und Heimat, Berlin, Oktober 2019. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  209. Delbaen, F./Schachermayer, W. (1994): A general version of the fundamental theorem of asset pricing, in: Mathematische Annalen, Vol. 300, Nr. 1, S. 463–520. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  210. Deville, L. (2008): Exchange Traded Funds, in: History, Trading and Research, C. Zopounidis, M. Doumpos, Handbook of Financial Engineering, Springer, S. 1–37. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  211. Engle, R.F. (1982): Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation, in: Econometrica, Vol. 50, No. 4, S. 987–1007. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  212. Fama, E. F./French, K. R. (1993): Common Risk Factors in the Returns on Stocks and Bonds, in: Journal of Financial Economics, Vol. 33, No. 1, S. 3–56. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  213. Giudici, P. (2018): Fintech risk management: A research challenge for artificial intelligence in finance, in: Frontiers in Artificial Intelligence, Vol. 1, S. 1–6. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  214. Goertzel, B. (2010): Toward a formal characterization of real-world general intelligence, in: 3d Conference on Artificial General Intelligence, Atlantis Press, S. 74–79. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  215. Harrison, J. M./Pliska, S. R. (1981): Martingales and stochastic integrals in the theory of continuous trading, in: Stochastic processes and their applications, Vol. 11, No. 3, S. 215–260. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  216. Hochreiter, S./Schmidhuber, J. (1997): Long short-term memory. Neural computation, Vol. 9, No. 8, S. 1735–1780. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  217. Hotho, A./Nürnberger, A./Paaß, G. (2005): A brief survey of Text Mining, in: Ldv Forum, Vol. 20, No. 1, S. 19–62. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  218. IBM Research (2013): Watson and the Jeopardy! Challenge, url https://www.youtube.com/watch?v=P18EdAKuC1U, accessed 21st November 2020. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  219. Juditsky, A./Hjalmarsson, H./Benveniste, A./Delyon, B./Ljung/L., Sjöberg/J./Zhang, Q. (1995): Nonlinear black-box models in system identification: Mathematical foundations, in: Automatica, Vol. 31, No. 12, S. 1725–1750. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  220. Kim, H. Y./Won, C. H. (2018): Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models, in: Expert Systems with Applications, Vol. 103, S. 25–37. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  221. Lintner, J. (1965): The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets, in: The Review of Economics and Statistics, MIT Press, Vol. 47, No. 1, S. 13–37. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  222. Luo, X./Zhang, J./Duan, W. (2013): Social Media and Firm Equity Value, in: Information Systems Research, Vol. 24, No. 1, S. 146–163. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  223. Markowitz, H. M. (1952): Portfolio Selection, in: Journal of Finance, Vol. 7, No. 1, S. 77–91. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  224. Markowitz, H. M. (1959): Portfolio selection: Efficient diversification of investment, Wiley, New York. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  225. Mitchell, T. (1997): Machine Learning, McGraw Hill, New York. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  226. Montavon, G./Samek, W./Müller, K. R. (2018): Methods for interpreting and understanding deep neural networks, in: Digital Signal Processing, Vol. 73, S. 1–15. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  227. Mossin, J. (1966): Equilibrium in a Capital Asset Market, in: Econometrica, Vol. 34, No. 4, S. 768–783. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  228. Murphy, K. P. (2012): Machine learning: A probabilistic perspective, MIT Press. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  229. Odean, T. (1998): Are Investors Reluctant to Realize Their Losses?, in: Journal of Finance. Vol. 53, No. 5, S. 1775–1798. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  230. Omohundro, S. M. (2007): The nature of self-improving artificial intelligence, in: Singularity Summit, Palo Alto, California. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  231. Pearlmutter, B. A. (1995): Gradient calculations for dynamic recurrent neural networks: A survey. IEEE Transactions on Neural networks, Vol. 6, No. 5, S. 1212–1228. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  232. Pennachin, C./Goertzel, B. (2007): Contemporary approaches to artificial general intelligence, in: B. Goertzel & C. Pennachin (Hrsg.), Artificial General Intelligence: AGIRI – Artificial General Intelligence Research Institute, Springer, Berlin, S. 1–28. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  233. Rabiner, L.R. (1989): A tutorial on hidden Markov models and selected applications in speech recognition, in: Proceedings of the IEEE, Vol. 77, No. 2, S. 257–286. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  234. Ribeiro, M. T./Singh, S./Guestrin, C. (2016): "Why should I trust you?" Explaining the predictions of any classifier, in: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, S. 1135–1144. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  235. Roncalli, T. (2013): Introduction to risk parity and budgeting, in: Chapman & Hall/CRC financial mathematics series, CRC Press, Boca Raton. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  236. Ross, S. (1976): The Arbitrage Theory of Capital Asset Pricing, in: Journal of Economic Theory, Vol. 13, No. 3, S. 341–360. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  237. Searle, J. R. (1980): Minds, brains, and programs, in: The Behavioral and Brain Sciences, Vol. 3, No. 3, S. 417–457. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  238. Sharpe, W. F. (1964): Capital asset prices: A theory of market equilibrium under conditions of risks, in: The Journal of Finance, Vol. 19, No. 3, S. 425–442. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  239. Silver, David/Huang, Aja/Maddison, Chris J./Guez, Arthur/Sifre, Laurent/van den Driessche/George et al. (2016): Mastering the game of go with deep neural networks and tree search, in: Nature, Vol. 529, S. 484–489. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  240. Silver, David/Hubert, Thomas/Schrittwieser, Julian/Antonoglou, Ioannis/Lai, Matthew/Guez, Arthur et al. (2017): Mastering chess and Shogi by Self-Play with a general reinforcement learning algorithm, in: arXiv:1712.01815, https://arxiv.org/abs/1712.01815, accessed: 28th July 2020. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  241. Sjöberg, Jonas/Zhang, Qinghua/Ljung, Lennart/Benveniste, Albert/Delyon, Bernard/Glorennec, Pierre-Yves et al. (1995): Nonlinear black-box modeling in system identification: A unified overview, in: Automatica (Journal of IFAC), Vol. 31, No. 12, S. 1691–1724. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  242. Statista (2020): In-depth: FinTech 2020: Statista Dgital Market Outlook, Statista, in https://de.statista.com/statistik/studie/id/45602/dokument/statista-report-fintech/, accessed: 17th March 2021. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  243. Taylor, B. J. (2006): Methods and procedures for the verification and validation of artificial neural networks, Springer Science & Business Media. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  244. Tetlock, P. C. (2007): Giving content to investor sentiment: The role of media in the stock market, in: The Journal of Finance, Vol. 62, No. 3, S. 1139–1168. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  245. Thaler, R. (1993): Advances in behavioral finance, Russell Sage Foundation. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  246. Turing, A. (1950): Computing machinery and intelligence, in: Mind, Vol. 59, No. 236, S. 433–460. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  247. Weber, R. H./Rainer, B. (2016): Regulierung von Robo-Advice, Neue Herausforderungen für Finanzintermediäre und Finanzmarktaufsichtsbehörden im Kontext der digitalen Anlageberatung und Vermögensverwaltung, in: Aktuelle juristische Praxis, Nr. 8, S. 1065–1078. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-411
  248. Abbasi, A./Sarker, S./Chiang, R. (2016): Big Data Research in Information Systems: Toward an Inclusive Research Agenda, in: Journal of the Association for Information Systems, Vol. 17, No. 2, S. I–XXXII. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  249. Anderson, J./ Rainie, L./Luchsinger, A. (2018): Artificial Intelligence and the Future of Humans. Pew Research Center. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  250. Acquisti, A./Grossklags, J. (2005): Privacy and rationality in individual decision-making, in: IEEE Security and Privacy Magazine, Vol 3, No.1, S. 26–33. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  251. Ajzen, I. (1991): The theory of planned behavior, in: Organizational Behavior and Human Decision Processes, Vol. 50, No. 2, S. 179–211. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  252. Ajzen, I./Fishbein, M. (1973): Attitudinal and normative variables as predictors of specific behavior, in: Journal of Personality and Social Psychology, Vol. 27, No. 1, S. 41–57. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  253. Antretter, T./Blohm, I./Siren, C./Grichnik, D./Malmstrom, M./Wincent, J. (2020): Do Algorithms Make Better — and Fairer — Investments Than Angel Investors?, in Harvard Business Review, https://hbr.org/2020/11/do-algorithms-make-better-and-fairer-investments-than-angel-investors. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  254. Arnold, V./Clark, N./Collier, P. A./Leech, S. A./Sutton, S. G. (2006): The Differential Use and Effect of Knowledge-Based System Explanations in Novice and Expert Judgment Decisions, in: MIS Quarterly, Vol. 30, No. 1, S. 79–97. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  255. Awad, E./Dsouza, S./Kim, R./Schulz, J./Henrich, J./Shariff, A.,... Rahwan, I. (2018): The Moral Machine experiment, in: Nature, Vol. 563, S. 59–64. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  256. Bao, W./Lianju, N./Yue, K. (2019): Integration of unsupervised and supervised machine learning algorithms for credit risk assessment, in: Expert Systems with Applications, Vol. 128, S. 301–315. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  257. Bhatt, G. D./Zaveri, J. (2002): The enabling role of decision support systems in organizational learning, in: Decision Support Systems, Vol 32, No. 3, S. 297–309. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  258. Briner, R. B./Denyer, D. (2012): Systematic Review and Evidence Synthesis as a Practice and Scholarship Tool, Oxford. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  259. Cath, C./Wachter, S./Mittelstadt, B./Taddeo, M./Floridi, L. (2018): Artificial Intelligence and the 'Good Society': The US, EU, and UK approach, in: Science and Engineering Ethics, Vol. 24, No. 2, S. 505–528. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  260. Chuang, T.‑T./Yadav, S. B. (1998): The development of an adaptive decision support system, in: Decision Support Systems, Vol. 24, No. 2, S. 73–87. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  261. Clark, T. D./Jones, M. C./Armstrong, C. P. (2007): The Dynamic Structure of Management Support Systems: Theory Development, Research Focus, and Direction, in: MIS Quarterly, Vol. 31, No. 3, S. 579–615. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  262. Constantiou, I. D./Kallinikos, J. (2015): New Games, New Rules: Big Data and the Changing Context of Strategy, in: Journal of Information Technology, Vol. 30, No. 1, S. 44–57. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  263. Davenport, T. H./Kirby J. (2016): Just How Smart Are Smart Machines?, in: MITSloan Management Review, Vol. 57, No. 3, S. 20–25. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  264. Davenport, T. H. (2018): The AI advantage: How to put the artificial intelligence revolution to work. Management on the cutting edge, Cambridge, Massachusetts. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  265. Davis, F. D. (1989): Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, in: MIS Quarterly, Vol. 13, No. 3, S. 319–340. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  266. Dellermann, D., Ebel, P., Söllner, M. et al. (2019): Hybrid Intelligence, in: Bus Inf Syst Eng Vol. 61, S. 637–643. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  267. Dhar, V. (2013): Data science and prediction, in: Communications of the ACM, Vol. 56, No. 12, S. 64–73. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  268. Druckenmiller, D./Acar, W. (2009): An Agent-Based Collaborative Approach to Graphing Causal Maps for Situation Formulation, in: Journal of the Association for Information Systems, Vol. 10, No. 3, S. 221–251. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  269. Duan, Y./Edwards, J. S./Dwivedi, Y. K. (2019): Artificial intelligence for decision-making in the era of Big Data – evolution, challenges and research agenda, in: International Journal of Information Management, Vol. 48, S. 63–71. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  270. Edwards, J. S./Duan, Y./Robins, P. C. (2000): An analysis of expert systems for business decision-making at different levels and in different roles, in: European Journal of Information Systems, Vol. 9, No. 1, S. 36–46. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  271. Faraj, S./Pachidi, S./Sayegh, K. (2018): Working and organizing in the age of the learning algorithm, in: Information and Organization, Vol. 28, No. 1, S. 62–70. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  272. Fountain, T./McCarthy B./Saleh T. (2019): Building the AI-Powered Organization Technology isn't the biggest challenge, Culture is, in: Harvard Business Review, July-August 2019. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  273. Giboney, J. S./Brown, S. A./Lowry, P. B./Nunamaker, J. F. (2015): User acceptance of knowledge-based system recommendations: Explanations, arguments, and fit, in: Decision Support Systems, Vol. 72, S. 1–10. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  274. Gregor, S./Benbasat, I. (1999): Explanations from Intelligent Systems: Theoretical Foundations and Implications for Practice, in: MIS Quarterly, Vol. 23, No. 4, S. 497–530. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  275. Hall, R. I. (1999): A Study of Policy Formation in Complex Organizations: Emulating Group Decision-Making with a Simple Artificial Intelligence and a System Model of Corporate Operations, in: Journal of Business Research, Vol. 45, No. 2, S. 157–171. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  276. Jacob, V. S./Moore, J. C./Whinston, A. B. (1988): Artificial Intelligence and the Management Science Practitioner: Rational Choice and Artificial Intelligence, in: Interfaces, Vol. 18, No. 4, S. 24–35. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  277. Jarrahi, M. H. (2018): Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision-making, in: Business Horizons, Vol. 61, No. 4, S. 577–586. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  278. Jensen, M. L./Lowry, P. B./Burgoon, J. K./Nunamaker, J. F. (2010): Technology Dominance in Complex Decision-making: The Case of Aided Credibility Assessment, in: Journal of Management Information Systems, Vol. 27, No. 1, S. 175–202. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  279. Kahneman, D./Tversky, A. (1979): Prospect Theory: An Analysis of Decision under Risk, in: Econometrica, Vol. 47, No. 2, S. 263–291. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  280. Keding (2020): Understanding the interplay of artificial intelligence and strategic management: four decades of research in review, in: Management Review Quarterly, https://doi.org/10.1007/s11301-020-00181-x Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  281. Kim, C. N./Yang, K. H./Kim, J. (2008): Human decision-making behavior and modeling effects, in: Decision Support Systems, Vol. 45, No. 3, S. 517–527. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  282. Kitchenham, B./Brereton, P. (2013): A systematic review of systematic review process research in software engineering, in: Information and Software Technology, Vol. 55, No. 12, S. 2049–2075. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  283. Kolbjørnsrud, V./Amico, R./Thomas, R. J. (2017): Partnering with AI: how organizations can win over skeptical managers, in: Strategy & Leadership, Vol. 45, No. 1, S. 37–43. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  284. Kratzwald, B./Ilić, S./Kraus, M./Feuerriegel, S./Prendinger, H. (2018): Deep learning for affective computing: Text-based emotion recognition in decision support, in: Decision Support Systems, Vol. 115, S. 24–35. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  285. Kuechler, W. L./Vaishnavi, V. (2006): So, Talk to Me: The Effect of Explicit Goals on the Comprehension of Business Process Narratives, in: MIS Quarterly, Vol. 30, No. 4, S. 961–979. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  286. Liao, S.‑h. (2003): Knowledge management technologies and applications—literature review from 1995 to 2002, in: Expert Systems with Applications, Vol. 25, No. 2, S. 155–164. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  287. Liebowitz, J. (2001): Knowledge management and its link to artificial intelligence, in: Expert Systems with Applications, Vol. 20, No. 1, S. 1–6. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  288. Limayem, M./DeSanctis, G. (2000): Providing Decisional Guidance for Multicriteria Decision-making in Groups, in: Information Systems Research, Vol. 11, No. 4, S. 386–401. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  289. Lismont, J./Vanthienen, J./Baesens, B./Lemahieu, W. (2017): Defining analytics maturity indicators: A survey approach, in: International Journal of Information Management, Vol. 37, No. 3, S. 114–124. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  290. Mahroof, K. (2019): A human-centric perspective exploring the readiness towards smart warehousing: The case of a large retail distribution warehouse, in: International Journal of Information Management, Vol. 45, S. 176–190. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  291. Martin, T. N. (2016): Smart Decisions, New York. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  292. McKinsey Global Institute (2018): Weltwirtschaft: Produktivitätskrise mit Digitalisierung und mehr Investitionen überwinden [Press release], Düsseldorf, Retrieved from https://www.mckinsey.de/~/media/McKinsey/Locations/Europe%20and%20Middle%20East/Deutschland/News/Presse/2018/2018-02-22/180222_pm_productivity.ashx, 20.01.2020 Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  293. Miller, T. (2019): Explanation in artificial intelligence: Insights from the social sciences, in: Artificial Intelligence, Vol. 267, S. 1–38. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  294. Min, H. (2010): Artificial intelligence in supply chain management: theory and applications, in: International Journal of Logistics Research and Applications, Vol. 13, No. 1, S. 13–39. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  295. Moser, J. G. (1986): Integration of artificial intelligence and simulation in a comprehensive decision-support system, in: SIMULATION, Vol. 47, No. 6, S. 223–229. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  296. Moskowitz, H./Drnevich, P./Ersoy, O./Altinkemer, K./Chaturvedi, A. (2011): Using Real-Time Decision Tools to Improve Distributed Decision-Making Capabilities in High-Magnitude Crisis Situations, in: Decision Sciences, Vol. 42, No. 2, S. 477–493. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  297. Moualek, I. (1997): An interactive decision-making approach based on intelligent systems to redraw district boundaries in regional planning, in: European Journal of Operational Research, Vol. 102, No. 2, S. 347–363. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  298. Nemati, H. R./Steiger, D. M./Iyer, L. S./Herschel, R. T. (2002): Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing, in: Decision Support Systems, Vol. 33, No. 2, S. 143–161. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  299. Nunes, I./Jannach, D. (2017): A systematic review and taxonomy of explanations in decision support and recommender systems, in: User Modeling and User-Adapted Interaction, Vol. 27, No. 3, S. 393–444. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  300. Parry, K./Cohen, M./Bhattacharya, S. (2016): Rise of the Machines, in: Group & Organization Management, Vol. 41, No. 5, S. 571–594. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  301. Phillips-Wren, G./Mora, M./Forgionne, G. A./Gupta, J. N.D. (2009): An integrative evaluation framework for intelligent decision support systems, in: European Journal of Operational Research, Vol. 195, No. 3, S. 642–652. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  302. Pieters, W. (2011): Explanation and trust: what to tell the user in security and AI?, in: Ethics and Information Technology, Vol. 13, No. 1, S. 53–64. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  303. Pomerol, J.‑C. (1997): Artificial intelligence and human decision-making, in: European Journal of Operational Research, Vol. 99, No. 1, S. 3–25. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  304. Quentin A. et al. (2018): Consumer Choice and Autonomy in the Age of Artificial Intelligence and Big Data, in: Customer Needs and Solutions, Vol. 5, No. 1, S. 28–37. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  305. Roetzel, P. G. (2019): Information overload in the information age: a review of the literature from business administration, business psychology, and related disciplines with a bibliometric approach and framework development, in: Business Research, Vol. 12, No. 2, S. 479–522. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  306. Russell, S./Norvig, P. (1995): A modern, agent-oriented approach to introductory artificial intelligence, in: ACM SIGART Bulletin, Vol. 6, No. 2, S. 24–26. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  307. Shapira, Z. (2010): Organizational Decision-making. Cambridge University Press. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  308. Sharma, R./Mithas, S./Kankanhalli, A. (2014): Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations, in: European Journal of Information Systems, Vol. 23, No. 4, S. 433–441. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  309. SHAO, Y. P. (1998): Perceived Impact and Diffusion of Expert Systems in Banking: An Exploratory Investigation, in: International Journal of Information Management, Vol. 18, No. 2, S. 139–156. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  310. Shreshta, Y. R./Ben-Menahem, S. M./Krogh, G. von (2019): Organizational Decision-Making Structures in the Age of Artificial Intelligence, in: California Management Review, Vol. 61, No. 4, S. 66–83. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  311. Sohn, K./Kwon, O. (2020): Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products, in: Telematics and Informatics, Vol. 47, S. 1–14. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  312. Stoica, I./Song, D./Popa, R. A./Patterson, D./Mahoney, M. W./Katz, R./Abbeel, P. (2017): A Berkeley View of Systems Challenges for AI. Retrieved from http://arxiv.org/pdf/1712.05855v1, 20.01.2020. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  313. Sutton, S. G./Holt, M./Arnold, V. (2016): “The reports of my death are greatly exaggerated”—Artificial intelligence research in accounting, in: International Journal of Accounting Information Systems, Vol. 22, S. 60–73. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  314. Watson, H. (2017): Preparing for the cognitive generation of decision support, in: MIS Quarterly Executive, Vol. 16, S. 153–169. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  315. Webster, J./Watson, R. T. (2002): Analyzing the Past to Prepare for the Future: Writing a Literature Review, in: MIS Quarterly, Vol. 26, No. 2, S. xiii–xxiii. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432
  316. Workman, M. (2005): Expert decision support system use, disuse, and misuse: a study using the theory of planned behavior, in: Computers in Human Behavior, Vol. 21, No. 2, S. 211–231. Open Google Scholar DOI: 10.5771/0042-059X-2021-3-432

Citation


Download RIS Download BibTex