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When this journal was founded in 1992 by Tudor Rickards and Susan Moger, there was no academic outlet available that addressed issues at the intersection of creativity and innovation. From zero to 1,163 records, from the new kid on the block to one of the leading journals in creativity and innovation management has been quite a journey, and we would like to reflect on the past 28 years and the intellectual and conceptual structure of Creativity and Innovation Management (CIM). Specifically, we highlight milestones and influential articles, identify how key journal characteristics evolved, outline the (co-)authorship structure, and finally, map the thematic landscape of CIM by means of a text-mining analysis. This study represents the first systematic and comprehensive assessment of the journal's published body of knowledge and helps to understand the journal's influence on the creativity and innovation management community. We conclude by discussing future topics and paths of the journal as well as limitations of our approach.
Dynamic pricing is considered a possibility to gain an advantage over competitors in modern online markets. The past advancements in Reinforcement Learning (RL) provided more capable algorithms that can be used to solve pricing problems. In this paper, we study the performance of Deep Q-Networks (DQN) and Soft Actor Critic (SAC) in different market models. We consider tractable duopoly settings, where optimal solutions derived by dynamic programming techniques can be used for verification, as well as oligopoly settings, which are usually intractable due to the curse of dimensionality. We find that both algorithms provide reasonable results, while SAC performs better than DQN. Moreover, we show that under certain conditions, RL algorithms can be forced into collusion by their competitors without direct communication.
In many businesses, firms are selling different types of products, which share mutual substitution effects in demand. To compute effective pricing strategies is challenging as the sales probabilities of each of a firm's products can also be affected by the prices of potential substitutes. In this paper, we analyze stochastic dynamic multi-product pricing models for the sale of perishable goods. To circumvent the limitations of time-consuming optimal solutions for highly complex models, we propose different relaxation techniques, which allow to reduce the size of critical model components, such as the state space, the action space, or the set of potential sales events. Our heuristics are able to decrease the size of those components by forming corresponding clusters and using subsets of representative elements. Using numerical examples, we verify that our heuristics make it possible to dramatically reduce the computation time while still obtaining close-to-optimal expected profits. Further, we show that our heuristics are (i) flexible, (ii) scalable, and (iii) can be arbitrarily combined in a mutually supportive way.
Der Untersuchungsgegenstand der vorliegenden Arbeit ist, die mit dem Begriff „Design Thinking“ verbundenen Diskurse zu bestimmen und deren Themen, Konzepte und Bezüge herauszuarbeiten. Diese Zielstellung ergibt sich aus den mehrfachen Widersprüchen und Vieldeutigkeiten, die die gegenwärtigen Verwendungen des Design-Thinking-Begriffs charakterisieren und den kohärenten Gebrauch in Wissenschaft und Wirtschaft erschweren. Diese Arbeit soll einen Beitrag dazu leisten, „Design Thinking“ in den unterschiedlichen Diskurszusammenhängen grundlegend zu verstehen und für zukünftige Verwendungen des Design-Thinking-Begriffs eine solide Argumentationsbasis zu schaffen.