%0 Conference Paper %1 ls_leimeister %A Dellermann, Dominik %B International Conference on Information Systems (ICIS) %D 2018 %K collective_intelligence decision_making decision_support_system dempub hybrid_intelligence itegpub itimpub machine_learning pub_dde uncertainty %T Accelerating Decisional Guidance in Entrepreneurship with Hybrid Intelligence %X Decision making in the context of entrepreneurship is a highly complex task. For dealing with such uncertain problems, decisional guidance proved to be a valuable approach to support entrepreneurs in making decisions. Mentoring and feedback from human experts and simple statistical methods emerge as dominant forms of providing decisional guidance. However, both individual human experts and existing decision support systems (DSS) have limited capabilities to provide optimal decisional guidance in such highly uncertain settings. While human experts have cognitive and resource constraints that prevent them from providing optimal guidance, computational DSS are frequently to deal with such complex problems. Consequently, the purpose of this dissertation is to provide prescriptive knowledge for novel forms of decisional guidance that support entrepreneurial decision makers by proposing collective intelligence and crowdsourcing (effective) as well as hybrid intelligence (effective and efficient) mechanism for efficient and effective decisional guidance. Following a design science research approach, I develop several artefacts such as system architectures and DSSs that are evaluated and provide innovative design knowledge for hybrid intelligence IS. The contribution of this dissertation is threefold. First, I provide an in-depth exploration of decisional guidance and its limitations in entrepreneurial decision making. Second, I propose collective intelligence and IT enabled crowdsourcing as a novel mechanism to guide entrepreneurs in creating their opportunities. Third, I conceptually develop hybrid intelligence as novel forms for making predictions under uncertainty. My research therefore provides prescriptive knowledge on the design of such hybrid intelligence architectures that are capable to predict uncertain outcomes. Finally, my results point towards a new class of DSS that might be particularly valuable in highly uncertain contexts.

@inproceedings{ls_leimeister, abstract = {Decision making in the context of entrepreneurship is a highly complex task. For dealing with such uncertain problems, decisional guidance proved to be a valuable approach to support entrepreneurs in making decisions. Mentoring and feedback from human experts and simple statistical methods emerge as dominant forms of providing decisional guidance. However, both individual human experts and existing decision support systems (DSS) have limited capabilities to provide optimal decisional guidance in such highly uncertain settings. While human experts have cognitive and resource constraints that prevent them from providing optimal guidance, computational DSS are frequently to deal with such complex problems. Consequently, the purpose of this dissertation is to provide prescriptive knowledge for novel forms of decisional guidance that support entrepreneurial decision makers by proposing collective intelligence and crowdsourcing (effective) as well as hybrid intelligence (effective and efficient) mechanism for efficient and effective decisional guidance. Following a design science research approach, I develop several artefacts such as system architectures and DSSs that are evaluated and provide innovative design knowledge for hybrid intelligence IS. The contribution of this dissertation is threefold. First, I provide an in-depth exploration of decisional guidance and its limitations in entrepreneurial decision making. Second, I propose collective intelligence and IT enabled crowdsourcing as a novel mechanism to guide entrepreneurs in creating their opportunities. Third, I conceptually develop hybrid intelligence as novel forms for making predictions under uncertainty. My research therefore provides prescriptive knowledge on the design of such hybrid intelligence architectures that are capable to predict uncertain outcomes. Finally, my results point towards a new class of DSS that might be particularly valuable in highly uncertain contexts.}, added-at = {2018-09-10T09:42:34.000+0200}, author = {Dellermann, Dominik}, biburl = {https://www.bibsonomy.org/bibtex/2ec86e1b3bd9138c3a5924dfecca6dad5/ls_leimeister}, booktitle = {International Conference on Information Systems (ICIS)}, eventdate = {13.12.2018-16.12.2018}, eventtitle = {International Conference on Information Systems (ICIS)}, interhash = {18e5b0b4c1e460c016ef445b54d8074e}, intrahash = {ec86e1b3bd9138c3a5924dfecca6dad5}, keywords = {collective_intelligence decision_making decision_support_system dempub hybrid_intelligence itegpub itimpub machine_learning pub_dde uncertainty}, timestamp = {2021-07-27T10:33:05.000+0200}, title = {Accelerating Decisional Guidance in Entrepreneurship with Hybrid Intelligence}, venue = {San Fransisco, USA}, year = 2018 }