Needs that are shared by members of an organization can trigger an organizational learning process. To a large extent, needs are implicitly anchored in organizations and people can hardly articulate them. In this article, we present Bewextra, a method that allows for identifying hidden needs in organizations. Thereby, we trigger a knowledge conversion process, which is similar to Nonaka's SECI-spiral. In two case studies, we present how our Bewextra-process is applied to projects in educational contexts in Austria. In a first case study, we show that a combination of learning from past and future experiences extend the scope of the overall outcome. Since learning from future experiences requires a distinct environment (enabling spaces), we present a second case study. Here, we conducted a Bewextra-process with a large number of participants (n > 170), focusing on learning from future experiences.

Innovation processes require organizations to transcend current boundaries. These include not only technological as well as social limitations but "above all" the way we address the future. We are used to face the future with our existing knowledge and experiences from the past. This strategy, however, can hardly lead to knowledge off the beaten path. We therefore suggest a new learning approach for organizations, which enables to literally envision a desired future scenario and thereby, allows for the creation of radical new knowledge. We argue that the created knowledge yields a higher degree of novelty and radicalness. Along with an enhanced theory of learning including learning from the future, we present our empirical findings from comparing the outputs of Learning from an Envisioned Future and learning from the past. For this purpose, we use data from two organizational learning projects; one, which was conducted with a high school in Austria and another one, which was conducted with members of the Austrian Economic Chamber. Our findings from both case studies suggest that Learning from an Envisioned Future does produce significantly more paradigm challenging knowledge compared to the output gained from conventional learning from past experiences. We conclude that the combination of both learning sources may lead to best learning outcomes in organizations.

付記する学位プログラム名: デザイン学大学院連携プログラム / 京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第22772号 / 工博第4771号 / 新制||工||1746(附属図書館) / 京都大学大学院工学研究科機械理工学専攻 / (主査)教授 椹木 哲夫, 教授 松原 厚, 教授 小森 雅晴 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM