@article{FilserTiberiusKrausetal.2020, author = {Filser, Matthias and Tiberius, Victor and Kraus, Sascha and Zeitlhofer, Tanita and Kailer, Norbert and M{\"u}ller, Adrian}, title = {Opportunity recognition}, series = {Entrepreneurship research journal}, volume = {13}, journal = {Entrepreneurship research journal}, number = {1}, publisher = {De Gruyter}, address = {Berlin}, issn = {2194-6175}, doi = {10.1515/erj-2020-0124}, pages = {1 -- 30}, year = {2020}, abstract = {This paper provides an overview of the ever-increasing literature on opportunity recognition, with a focus on its antecedents and determinants. With a two-step research approach, a bibliometric analysis and a systematic literature review, we structure the current research in this field. By using bibliometric techniques, we analyzed 161 publications and, consequently, clustered the 30 most influential references. Apart from economic theories and the role of opportunity recognition in entrepreneurship, a strong research focus is on antecedents of opportunity recognition. Therefore, in our subsequent literature review, we focus on determinants which influence opportunity recognition. We find that the opportunity recognition process is influenced by various personal, organizational and environmental factors. We conclude with a research outlook for future research opportunities on opportunity recognition.}, language = {en} } @article{HesselbarthAlnoorTiberius2023, author = {Hesselbarth, Imke and Alnoor, Alhamzah and Tiberius, Victor}, title = {Behavioral strategy}, series = {Management decision}, volume = {61}, journal = {Management decision}, number = {9}, publisher = {Emerald}, address = {Bingley}, issn = {0025-1747}, doi = {10.1108/MD-09-2021-1274}, pages = {2740 -- 2756}, year = {2023}, abstract = {Purpose: Behavioral strategy, as a cognitive- and social-psychological view on strategic management, has gained increased attention. However, its conceptualization is still fuzzy and deserves an in-depth investigation. The authors aim to provide a holistic overview and classification of previous research and identify gaps to be addressed in future research. Design/methodology/approach: The authors conducted a systematic literature review on behavioral strategy. The final sample includes 46 articles from leading management journals, based on which the authors develop a research framework. Findings: The results reveal cognition and traits as major internal factors. Besides, organizational and environmental contingencies are major external factors of behavioral strategy. Originality/value: To the authors' best knowledge, this is the first holistic systematic literature review on behavioral strategy, which categorizes previous research.}, language = {en} } @article{KhawAlnoorAlAbrrowetal.2022, author = {Khaw, Khai Wah and Alnoor, Alhamzah and Al-Abrrow, Hadi and Tiberius, Victor and Ganesan, Yuvaraj and Atshan, Nadia A.}, title = {Reactions towards organizational change}, series = {Current psychology}, journal = {Current psychology}, publisher = {Springer}, address = {New York}, issn = {1046-1310}, doi = {10.1007/s12144-022-03070-6}, pages = {1 -- 24}, year = {2022}, abstract = {Regardless of the prevalence and value of change initiatives in contemporary organizations, these often face resistance by employees. This resistance is the outcome of change recipients' cognitive and behavioral reactions towards change. To better understand the causes and effects of reactions to change, a holistic view of prior research is needed. Accordingly, we provide a systematic literature review on this topic. We categorize extant research into four major and several subcategories: micro and macro reactions. We analyze the essential characteristics of the emerging field of change reactions along research issues and challenges, benefits of (even negative) reactions, managerial implications, and propose future research opportunities.}, language = {en} } @article{PanzerBender2021, author = {Panzer, Marcel and Bender, Benedict}, title = {Deep reinforcement learning in production systems}, series = {International Journal of Production Research}, volume = {13}, journal = {International Journal of Production Research}, number = {60}, publisher = {Taylor \& Francis}, address = {London}, issn = {1366-588X}, doi = {10.1080/00207543.2021.1973138}, year = {2021}, abstract = {Shortening product development cycles and fully customizable products pose major challenges for production systems. These not only have to cope with an increased product diversity but also enable high throughputs and provide a high adaptability and robustness to process variations and unforeseen incidents. To overcome these challenges, deep Reinforcement Learning (RL) has been increasingly applied for the optimization of production systems. Unlike other machine learning methods, deep RL operates on recently collected sensor-data in direct interaction with its environment and enables real-time responses to system changes. Although deep RL is already being deployed in production systems, a systematic review of the results has not yet been established. The main contribution of this paper is to provide researchers and practitioners an overview of applications and to motivate further implementations and research of deep RL supported production systems. Findings reveal that deep RL is applied in a variety of production domains, contributing to data-driven and flexible processes. In most applications, conventional methods were outperformed and implementation efforts or dependence on human experience were reduced. Nevertheless, future research must focus more on transferring the findings to real-world systems to analyze safety aspects and demonstrate reliability under prevailing conditions.}, language = {en} } @misc{PanzerBenderGronau2022, author = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert}, title = {Neural agent-based production planning and control}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, issn = {1867-5808}, doi = {10.25932/publishup-60477}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-604777}, pages = {26}, year = {2022}, abstract = {Nowadays, production planning and control must cope with mass customization, increased fluctuations in demand, and high competition pressures. Despite prevailing market risks, planning accuracy and increased adaptability in the event of disruptions or failures must be ensured, while simultaneously optimizing key process indicators. To manage that complex task, neural networks that can process large quantities of high-dimensional data in real time have been widely adopted in recent years. Although these are already extensively deployed in production systems, a systematic review of applications and implemented agent embeddings and architectures has not yet been conducted. The main contribution of this paper is to provide researchers and practitioners with an overview of applications and applied embeddings and to motivate further research in neural agent-based production. Findings indicate that neural agents are not only deployed in diverse applications, but are also increasingly implemented in multi-agent environments or in combination with conventional methods — leveraging performances compared to benchmarks and reducing dependence on human experience. This not only implies a more sophisticated focus on distributed production resources, but also broadening the perspective from a local to a global scale. Nevertheless, future research must further increase scalability and reproducibility to guarantee a simplified transfer of results to reality.}, language = {en} } @misc{PanzerBenderGronau2021, author = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert}, title = {Deep reinforcement learning in production planning and control}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, issn = {2701-6277}, doi = {10.25932/publishup-60572}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-605722}, pages = {13}, year = {2021}, abstract = {Increasingly fast development cycles and individualized products pose major challenges for today's smart production systems in times of industry 4.0. The systems must be flexible and continuously adapt to changing conditions while still guaranteeing high throughputs and robustness against external disruptions. Deep reinforcement learning (RL) algorithms, which already reached impressive success with Google DeepMind's AlphaGo, are increasingly transferred to production systems to meet related requirements. Unlike supervised and unsupervised machine learning techniques, deep RL algorithms learn based on recently collected sensorand process-data in direct interaction with the environment and are able to perform decisions in real-time. As such, deep RL algorithms seem promising given their potential to provide decision support in complex environments, as production systems, and simultaneously adapt to changing circumstances. While different use-cases for deep RL emerged, a structured overview and integration of findings on their application are missing. To address this gap, this contribution provides a systematic literature review of existing deep RL applications in the field of production planning and control as well as production logistics. From a performance perspective, it became evident that deep RL can beat heuristics significantly in their overall performance and provides superior solutions to various industrial use-cases. Nevertheless, safety and reliability concerns must be overcome before the widespread use of deep RL is possible which presumes more intensive testing of deep RL in real world applications besides the already ongoing intensive simulations.}, language = {en} } @article{PanzerBenderGronau2022, author = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert}, title = {Neural agent-based production planning and control}, series = {Journal of Manufacturing Systems}, volume = {65}, journal = {Journal of Manufacturing Systems}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0278-6125}, doi = {10.1016/j.jmsy.2022.10.019}, pages = {743 -- 766}, year = {2022}, abstract = {Nowadays, production planning and control must cope with mass customization, increased fluctuations in demand, and high competition pressures. Despite prevailing market risks, planning accuracy and increased adaptability in the event of disruptions or failures must be ensured, while simultaneously optimizing key process indicators. To manage that complex task, neural networks that can process large quantities of high-dimensional data in real time have been widely adopted in recent years. Although these are already extensively deployed in production systems, a systematic review of applications and implemented agent embeddings and architectures has not yet been conducted. The main contribution of this paper is to provide researchers and practitioners with an overview of applications and applied embeddings and to motivate further research in neural agent-based production. Findings indicate that neural agents are not only deployed in diverse applications, but are also increasingly implemented in multi-agent environments or in combination with conventional methods — leveraging performances compared to benchmarks and reducing dependence on human experience. This not only implies a more sophisticated focus on distributed production resources, but also broadening the perspective from a local to a global scale. Nevertheless, future research must further increase scalability and reproducibility to guarantee a simplified transfer of results to reality.}, language = {en} } @inproceedings{PanzerBenderGronau2021, author = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert}, title = {Deep reinforcement learning in production planning and control}, series = {Proceedings of the Conference on Production Systems and Logistics}, booktitle = {Proceedings of the Conference on Production Systems and Logistics}, publisher = {publish-Ing.}, address = {Hannover}, issn = {2701-6277}, doi = {10.15488/11238}, pages = {535 -- 545}, year = {2021}, abstract = {Increasingly fast development cycles and individualized products pose major challenges for today's smart production systems in times of industry 4.0. The systems must be flexible and continuously adapt to changing conditions while still guaranteeing high throughputs and robustness against external disruptions. Deep rein- forcement learning (RL) algorithms, which already reached impressive success with Google DeepMind's AlphaGo, are increasingly transferred to production systems to meet related requirements. Unlike supervised and unsupervised machine learning techniques, deep RL algorithms learn based on recently collected sensor- and process-data in direct interaction with the environment and are able to perform decisions in real-time. As such, deep RL algorithms seem promising given their potential to provide decision support in complex environments, as production systems, and simultaneously adapt to changing circumstances. While different use-cases for deep RL emerged, a structured overview and integration of findings on their application are missing. To address this gap, this contribution provides a systematic literature review of existing deep RL applications in the field of production planning and control as well as production logistics. From a performance perspective, it became evident that deep RL can beat heuristics significantly in their overall performance and provides superior solutions to various industrial use-cases. Nevertheless, safety and reliability concerns must be overcome before the widespread use of deep RL is possible which presumes more intensive testing of deep RL in real world applications besides the already ongoing intensive simulations.}, language = {en} } @misc{RoeschTiberiusKraus2023, author = {R{\"o}sch, Nicolas and Tiberius, Victor and Kraus, Sascha}, title = {Design thinking for innovation}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, number = {7}, issn = {1460-1060}, doi = {10.25932/publishup-60834}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-608341}, pages = {19}, year = {2023}, abstract = {Purpose - Design thinking has become an omnipresent process to foster innovativeness in various fields. Due to its popularity in both practice and theory, the number of publications has been growing rapidly. The authors aim to develop a research framework that reflects the current state of research and allows for the identification of research gaps. Design/methodology/approach - The authors conduct a systematic literature review based on 164 scholarly articles on design thinking. Findings - This study proposes a framework, which identifies individual and organizational context factors, the stages of a typical design thinking process with its underlying principles and tools, and the individual as well as organizational outcomes of a design thinking project. Originality/value - Whereas previous reviews focused on particular aspects of design thinking, such as its characteristics, the organizational culture as a context factor or its role on new product development, the authors provide a holistic overview of the current state of research.}, language = {en} } @article{RoeschTiberiusKraus2023, author = {R{\"o}sch, Nicolas and Tiberius, Victor and Kraus, Sascha}, title = {Design thinking for innovation}, series = {European journal of innovation management}, volume = {26}, journal = {European journal of innovation management}, number = {7}, publisher = {Emerald}, address = {Bingley}, issn = {1460-1060}, doi = {10.1108/EJIM-03-2022-0164}, pages = {160 -- 176}, year = {2023}, abstract = {Purpose - Design thinking has become an omnipresent process to foster innovativeness in various fields. Due to its popularity in both practice and theory, the number of publications has been growing rapidly. The authors aim to develop a research framework that reflects the current state of research and allows for the identification of research gaps. Design/methodology/approach - The authors conduct a systematic literature review based on 164 scholarly articles on design thinking. Findings - This study proposes a framework, which identifies individual and organizational context factors, the stages of a typical design thinking process with its underlying principles and tools, and the individual as well as organizational outcomes of a design thinking project. Originality/value - Whereas previous reviews focused on particular aspects of design thinking, such as its characteristics, the organizational culture as a context factor or its role on new product development, the authors provide a holistic overview of the current state of research.}, language = {en} }