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The Circular Economy (CE) - based on five principles (reduce, reuse, refurbish, repair, and recycle) - has received increased attention in both academia and practice in recent years. The transition to CE by public and private organizations can be seen as an entrepreneurial act encompassing their strategic policies, business models, structures, and processes. Little is known about the involvement of employees of organizations making this transition. Therefore, this study investigates the influence of organizations’ commitment to the five CE principles on their employees’ perceptions of the usefulness, ease of implementation, and acceptability of the principles. The method used is exploratory, a mixed-method approach combining PLS-SEM and fsQCA. This research contributes to the field by developing a unified theoretical perspective on the entrepreneurial context. It also highlights the impact of CE principles on organizations that are transitioning to more sustainable development.
Purpose
This paper provides a systematization of the existing body of literature on both employee participation goals and the intervention formats in the context of organizational change. Furthermore, degrees of employee involvement that the intervention formats address are identified and related to the goals of employee participation. On this basis, determinants of employee involvement and participation in the context of digital transformation are unveiled.
Design/methodology/approach
Based on a systematic literature review the authors structure and relate employee participation goals and formats. Through a workshop with expert practitioners, the authors transfer and enhance these theoretical findings in the context of digital transformation. Experts rated the three most important goals and identified accompanying success factors, barriers and effects.
Findings
The results show that it is not necessarily the degree of involvement but a context-specific selection of measures, the quality of their implementation as well as the actual uptake of suggestions and activities developed by employees that contribute to employees accepting and participating in goal-directed transformations. Moreover, employees must have sufficient information and time for their participation in transformation processes.
Originality/value
This paper is based on a transformative approach, combining literature analysis to identify formats and goals of employee participation with experiential knowledge of digital transformation practitioners. In addition to relating intervention formats to goals pursued in organizational change processes, empirical and experiential perspectives are used to identify three very relevant goals and respective determinants in digital transformation processes.
German and European migration policy operates in permanent crisis mode. Sudden increases in irregular immigration create a sense of loss of control, which is instrumentalised by populist forces. This has generated great interest in quantitative migration predictions. High expectations are placed in the AI-based tools currently under devel­op­ment for forecasting irregular migration. The potential applications of these tools are manifold. They range from managing and strengthening the EU's reception capacity and border protections to configuring humanitarian aid provision and longer-term planning of development programmes. There is a significant gap between the expectations placed in the new instruments and their practical utility. Technical limits exist, medium-term forecasts are methodologically implausible, and channels for feeding the results into political decision-making processes are lacking. The great demand for predictions is driven by the political functions of migration prediction, which include its uses in political communication, funding acquisition and legitimisation of political decisions. Investment in the quality of the underlying data will be more productive than developing a succession of new prediction tools. Funding for applications in emergency relief and development cooperation should be prioritised. Crisis early warning and risk analysis should also be strengthened and their networking improved.
Die deutsche und europäische Migrationspolitik befindet sich im permanenten Krisenmodus. Plötzliche Anstiege ungeregelter Zuwanderung nähren ein Gefühl von Kontrollverlust, das wiederum von populistischen Kräften instrumentalisiert wird. Daher hat die Politik großes Interesse an quantitativen Migrationsprognosen. Besondere Erwartungen wecken KI-gestützte Instrumente zur Vorhersage ungeregelter Wanderungsbewegungen, wie sie zurzeit entwickelt werden. Die Anwendungsfelder dieser Instrumente sind vielfältig. Sie reichen von einer Stärkung der Aufnahmekapazitäten in der EU über die präventive Verschärfung von Grenzschutzmaßnahmen und eine bedarfsgerechte Bereitstellung von Ressourcen in humanitären Krisen bis zur längerfristigen entwicklungspolitischen Programmplanung. Allerdings besteht eine deutliche Kluft zwischen den Erwartungen an die neuen Instrumente und ihrem praktischen Mehrwert. Zum einen sind die technischen Möglichkeiten begrenzt, und mittelfristige Vorhersagen zu ungeregelten Wanderungen sind methodisch kaum möglich. Zum anderen mangelt es an Verfahren, um die Ergebnisse in politische Entscheidungsprozesse einfließen zu lassen. Die hohe Nachfrage nach Prognosen erklärt sich aus den politischen Funktionen quantitativer Migrationsvorhersage - beispielsweise ihrem Potential für die politische Kommunikation, die Mitteleinwerbung und die Legitimierung politischer Entscheidungen. Investitionen in die Qualität der den Prognosen zugrunde liegenden Daten sind sinnvoller als die Entwicklung immer neuer Instrumente. Bei der Mittelvergabe für Prognosen sollten Anwendungen in der Nothilfe und der Entwicklungszusammenarbeit priorisiert werden. Zudem sollten die Krisenfrüherkennung und die Risikoanalyse gestärkt werden, und die beteiligten Akteure sollten sich besser vernetzen.
Soziales Vertragsrecht
(2023)
The motion picture industry is subject to extensive business and management research conducted on a wide range of topics. Due to high research productivity, it is challenging to keep track of the abundance of publications. Against this background, we employ a bibliographic coupling analysis to gain a comprehensive understanding of current research topics. The following themes were defined: Key factors for success, word of mouth and social media, organizational and pedagogical dimensions, advertising—product placement and online marketing, tourism, the influence of data, the influence of culture, revenue maximization and purchase decisions, and the perception and identification of audiences. Based on the cluster analysis, we suggest the following future research opportunities: Exploring technological innovations, especially the influence of social media and streaming platforms in the film industry; the in-depth analysis of the use of artificial intelligence in film production, both in terms of its creative potential and ethical and legal challenges; the exploration of the representation of wokeness and minorities in films and their cultural and economic significance; and, finally, a detailed examination of the long-term effects of the COVID-19 pandemic and other crises on the film industry, especially in terms of changed consumption habits and structural adjustments.
‘Steadfast and unreserved’
(2023)
Several lines of research have demonstrated spatial-numerical associations in both adults and children, which are thought to be based on a spatial representation of numerical information in the form of a mental number line. The acquisition of increasingly precise mental number line representations is assumed to support arithmetic learning in children. It is further suggested that sensorimotor experiences shape the development of number concepts and arithmetic learning, and that mental arithmetic can be characterized as “motion along a path” and might constitute shifts in attention along the mental number line. The present study investigated whether movements in physical space influence mental arithmetic in primary school children, and whether the expected effect depends on concurrency of body movements and mental arithmetic. After turning their body towards the left or right, 48 children aged 8 to 10 years solved simple subtraction and addition problems. Meanwhile, they either walked or stood still and looked towards the respective direction. We report a congruency effect between body orientation and operation type, i.e., higher performance for the combinations leftward orientation and subtraction and rightward orientation and addition. We found no significant difference between walking and looking conditions. The present results suggest that mental arithmetic in children is influenced by preceding sensorimotor cues and not necessarily by concurrent body movements.
HARE
(2023)
Sensor-based human activity recognition is becoming ever more prevalent. The increasing importance of distinguishing human movements, particularly in healthcare, coincides with the advent of increasingly compact sensors. A complex sequence of individual steps currently characterizes the activity recognition pipeline. It involves separate data collection, preparation, and processing steps, resulting in a heterogeneous and fragmented process. To address these challenges, we present a comprehensive framework, HARE, which seamlessly integrates all necessary steps. HARE offers synchronized data collection and labeling, integrated pose estimation for data anonymization, a multimodal classification approach, and a novel method for determining optimal sensor placement to enhance classification results. Additionally, our framework incorporates real-time activity recognition with on-device model adaptation capabilities. To validate the effectiveness of our framework, we conducted extensive evaluations using diverse datasets, including our own collected dataset focusing on nursing activities. Our results show that HARE’s multimodal and on-device trained model outperforms conventional single-modal and offline variants. Furthermore, our vision-based approach for optimal sensor placement yields comparable results to the trained model. Our work advances the field of sensor-based human activity recognition by introducing a comprehensive framework that streamlines data collection and classification while offering a novel method for determining optimal sensor placement.