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#Gesellschaftslehre 9/10
(2023)
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.
Higher eco-efficiency will not be enough to slow global warming caused by climate change. To keep global warming to 2 degrees, people also need to reduce their consumption. At present, however, many who would be able to do so seem unwilling to comply. Given the threats of a runaway climate change, urgent measures are needed to promote less personal consumption. This study, therefore, examines whether social marketing consume-less appeals can be used to encourage consumers to voluntarily abstain from consumption. As part of an online experiment with nearly 2000 randomly sampled users of an online platform for sustainable consumption, we tested the effectiveness of five different “consume-less” appeals based on traditional advertising formats (including emotional, informational, and social claims). The study shows that consume-less appeals are capable of limiting personal desire to buy. However, significant differences in the effectiveness of the appeal formats used in this study were observed. In addition, we found evidence of rebound effects, which leads us to critically evaluate the overall potential of social marketing to promote more resource-conserving lifestyles. While commercial consumer-free appeals have previously been studied (e.g., Patagonia’s “Don’t Buy This Jacked”), this study on the effectiveness of non-commercial consume-free appeals is novel and provides new insights.
In social networks or, more specifically, online communities on tech-products, opinion leaders are important sources of advice for other consumers in the adoption and diffusion of new products. However, possibilities for potential users to exert their influence on opinion leadership are ignored. This study determines whether and how lead users may serve as opinion leaders in social networks and advise other consumers in the adoption and diffusion of new products. Our survey with 308 users in the Xiaomi and Huawei communities suggests that higher lead userness is positively and significantly associated with the likelihood of opinion giving and passing. Product-possessing innovativeness has a higher impact compared with information-possessing innovativeness. Product involvement does not enhance the effect of information-possessing innovativeness. The findings provide a better understanding of the formation of opinion leadership in social networks for an accelerated diffusion of new products.
Nowadays, innovative and entrepreneurial activities and their actors are embedded in interdependent systems to drive joint value creation. Innovation ecosystems and entrepreneurial ecosystems have become established system-level concepts in management research to explain how value transpires between different actors and institutions in distinct contexts. Despite the popularity of the concepts, researchers have critiqued their theoretical depth, conceptual distinctiveness, as well as operationalization and measurement (Autio & Thomas, 2022; Klimas & Czakon, 2022). Furthermore, in light of current-day challenges, research has yet to address how context impacts innovation and entrepreneurial ecosystems and their actors and elements (Wurth et al., 2022).
The aim of this cumulative thesis is to provide a deeper understanding of the conceptualization, operationalization, and measurement of innovation and entrepreneurial ecosystems and investigate how contextual factors can influence the overall ecosystem and its key actors. To this end, bibliometric and empirical-qualitative methods, as well as narrative and systematic literature reviews, are employed. After introducing the research scope and key concepts in Chapter 1, a systematic literature review to operationalize and measure the concept of innovation ecosystems is conducted, and an integrative framework of its composition is introduced in Chapter 2. In Chapter 3, the innovation journal network is outlined by means of science mapping to determine current and emerging research areas characterizing innovation studies. In Chapters 4 and 5, the interplay between the temporal context of the Covid-19 pandemic and the spatial context of entrepreneurial ecosystems is assessed by focusing on the role of organizational resilience and affordances. The findings shed new light on the dynamics and boundaries of entrepreneurial ecosystems as they move between the spatial and digital realm. Building on this, an integrative framework of digital entrepreneurial ecosystems is presented in Chapter 6. The concluding Chapter 7 summarizes my thesis’s conceptual, theoretical, and empirical insights, highlighting implications, limitations, and promising future research avenues.
The findings of this cumulative thesis contribute to the theoretical and conceptual advancement of ecosystems in innovation and entrepreneurship by providing insights into the measurement and operationalization of its elements. Furthermore, the results show that contextual factors, such as crisis events or institutional circumstances, influence innovation and entrepreneurial ecosystems and their actors, calling for a more nuanced consideration of ecosystem configurations and dynamics. By drawing from the theory of affordances, the elements that actually afford value to the actors and how they shift between the physical and digital realm are portrayed. Based on these findings, this thesis introduces novel frameworks and conceptual advancements of the configurations and boundaries of innovation and (digital) entrepreneurial ecosystems, laying the foundation for a renewed understanding of how to design, orchestrate, and evaluate ecosystems today and in the future.
Process mining (PM) has established itself in recent years as a main method for visualizing and analyzing processes. However, the identification of knowledge has not been addressed adequately because PM aims solely at data-driven discovering, monitoring, and improving real-world processes from event logs available in various information systems. The following paper, therefore, outlines a novel systematic analysis view on tools for data-driven and machine learning (ML)-based identification of knowledge-intensive target processes. To support the effectiveness of the identification process, the main contributions of this study are (1) to design a procedure for a systematic review and analysis for the selection of relevant dimensions, (2) to identify different categories of dimensions as evaluation metrics to select source systems, algorithms, and tools for PM and ML as well as include them in a multi-dimensional grid box model, (3) to select and assess the most relevant dimensions of the model, (4) to identify and assess source systems, algorithms, and tools in order to find evidence for the selected dimensions, and (5) to assess the relevance and applicability of the conceptualization and design procedure for tool selection in data-driven and ML-based process mining research.
Faced with the triad of time-cost-quality, the realization of production tasks under economic conditions is not trivial. Since the number of Artificial-Intelligence-(AI)-based applications in business processes is increasing more and more nowadays, the efficient design of AI cases for production processes as well as their target-oriented improvement is essential, so that production outcomes satisfy high quality criteria and economic requirements. Both challenge production management and data scientists, aiming to assign ideal manifestations of artificial neural networks (ANNs) to a certain task. Faced with new attempts of ANN-based production process improvements [8], this paper continues research about the optimal creation, provision and utilization of ANNs. Moreover, it presents a mechanism for AI case-based reasoning for ANNs. Experiments clarify continuously improving ANN knowledge bases by this mechanism empirically. Its proof-of-concept is demonstrated by the example of four production simulation scenarios, which cover the most relevant use cases and will be the basis for examining AI cases on a quantitative level.
Scholars have argued that visionary leadership is an effective tool to motivate followers because it provides them with meaning and purpose. However, previous research tells us little about which leaders and under which circumstances leaders engage in visionary leadership. We draw on theories of human and social capital to argue that leader work centrality is an important antecedent of visionary leadership, and especially so for leaders with low organizational tenure. Moreover, we propose that visionary leadership then provides followers with meaningfulness and thereby decreases their turnover intentions. Our predictions were confirmed by data from a two-wave, lagged-design field study with 101 leader-follower dyads. Overall, our research identifies an important antecedent of visionary leadership, a specific situation in which this antecedent is particularly important, and provides empirical evidence for why visionary leadership can bind followers to an organization.
Behavioral strategy
(2023)
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.