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Emotions in strategy
No full textThis literature review focuses on the role of emotions in behavioral strategy research. Emotions arise from various appraisals and can be viewed from different perspectives, from individual to interpersonal, group, and organizational levels. The relationship between emotions and strategy is crucial because strategic shifts can evoke emotions that impact the success of strategy implementation. While the significance of emotions in the workplace has often been overlooked in past strategy discussions, recent research has started exploring innovative concepts, such as emotional aperture, to understand the interplay between emotions and strategy. This includes considerations of hierarchy levels, leadership styles, company size, and global contexts
Methodical approach to the introduction of asset administration shell in learning factories
No full textThe Asset Administration Shell (AAS) represents a standardized digital representation of an asset facilitating the seamless combination of physical and digital objects in Industry 4.0. Originally introduced within the RAMI 4.0 framework, the AAS plays a pivotal role in achieving the core objectives of Industry 4.0 by introducing interoperable data exchange across different assets, life cycles, and value chains.
Delivering central requirements for the Industry 4.0 vision the importance of AAS for the transition towards smart factories is evident. However, due to its abstract nature, the AAS advantages, and implementation require explanation. Incorporating this technology into educational environments can provide this explanation by allowing students to grasp the importance of interoperability in advanced digital manufacturing settings. However, currently, literature on the utilization of AAS within the infrastructure and learning concepts of learning factories is missing.
To address this challenge and enable learning factories to effectively teach the principles of AAS, this paper describes an approach for introducing the AAS into learning factory environments. This approach entails the technical introduction of digital asset representations into the process flow of existing assemblies as well as a workshop illustrating the importance of the interoperability provided by the AAS in Industry 4.0 factories.
This paper serves as a foundational guide for introducing AAS into learning factories by underscoring the vital role of AAS in the education of future industry professionals and the realization of smart factories in the era of Industry 4.0
The principle of refurbish in circular economy : products better than new
No full textWith rapidly increasing demands for sustainable products, the principle of refurbish plays a vital role as one building block of sustainable supply chain management. It refers to the professional general overhaul of products for reuse to extract their maximum value. This microchapter shows the immense potential for saving CO2, water and e-waste along the entire value chain when using refurbed products. In addition, the refurbishing process and the most important trends in the market are presented. A case study from Royal Philips, a global leader in health technology, illustrates which actions are effective when educating customers to adopt sustainable behaviour by purchasing refurbished products. The "Better Than New" campaign to create awareness for Philips’ refurbished products serves as an example
Leveraging organizational knowledge to develop agility and improve performance: the role of ambidexterity
No full textPurpose
As a response to the increased frequency of disruptive events and intense competition, organizational agility has become a key concept in organizational research. Fostering organizational agility requires leveraging knowledge that exists both outside (exploration) and inside (exploitation) the organization. This research tests the so-called ambidexterity hypothesis, which claims that a balance between exploration and exploitation leads to increased organizational outcomes, including the development of organizational agility. Complementing previously established measurement models on ambidexterity, this research proposes an alternative measurement model to analyze how ambidexterity can enhance organizational agility and, indirectly, performance, taking into consideration the moderating effect of environmental competitiveness.
Design/methodology/approach
A review of existing measurement models for ambidexterity shows that tension, a crucial aspect of ambidexterity, is often neglected. The authors, therefore, develop a new measurement model of ambidexterity to incorporate ambidexterity-induced tension. Using this measurement model, they examine the effect of ambidexterity on the development of entrepreneurial and adaptive agility as well as performance.
Findings
Ambidexterity positively influences both entrepreneurial and adaptive agility, indicating that a balance between exploration and exploitation has superior organizational effects. This finding confirms the ambidexterity hypothesis with respect to organizational agility. Furthermore, both entrepreneurial and adaptive agility drive organizational performance. These two indirect effects via agility fully mediate the impact of ambidexterity on organizational performance. Finally, environmental competitiveness positively moderates the relationship between ambidexterity and adaptive agility.
Originality/value
The findings extend research on ambidexterity by showing its positive effects on organizational agility. Furthermore, the study proposes an alternative operationalization to capture the ambidexterity construct that may lay the groundwork for further applications of the ambidexterity concept
Comparative study of applying signal processing techniques on ballistocardiogram in detecting J-Peak using Bi-LSTM Model
No full textCardiovascular diseases (CVD) are leading contributors to global mortality, necessitating advanced methods for vital sign monitoring. Heart Rate Variability (HRV) and Respiratory Rate, key indicators of cardiovascular health, are traditionally monitored via Electrocardiogram (ECG). However, ECG's obtrusiveness limits its practicality, prompting the exploration of Ballistocardiography (BCG) as a non-invasive alternative. BCG records the mechanical activity of the body with each heartbeat, offering a contactless method for HRV monitoring. Despite its benefits, BCG signals are susceptible to external interference and present a challenge in accurately detecting J-Peaks. This research uses advanced signal processing and deep learning techniques to overcome these limitations. Our approach integrates accelerometers for long-term BCG data collection during sleep, applying Discrete Wavelet Transforms (DWT) and Ensemble Empirical Mode Decomposition (EEMD) for feature extraction. The Bi-LSTM model, leveraging these features, enhances heartbeat detection, offering improved reliability over traditional methods. The study's findings indicate that the combined use of DWT, EEMD, and Bi-LSTM for J-Peak detection in BCG signals is effective, with potential applications in unobtrusive long-term cardiovascular monitoring. Our results suggest that this methodology could contribute to HRV monitoring, particularly in home settings, enhancing patient comfort and compliance
Comparison between cure kinetics by means of dynamic rheology and DSC of formaldehyde-based wood adhesives
No full textComparative analysis of the chemical and rheological curing kinetics of formaldehyde-based wood adhesives is crucial for assessing their respective performance. Differential scanning calorimetry (DSC) and rheometry are the conventional techniques used for monitoring the curing processes leading to crosslinking polymerization of the adhesives. However, the direct comparison of these techniques is inappropriate due to the intrinsic differences in their underlying procedures. To address this challenge, the two adhesive samples were sequentially cured, firstly with rheometry and followed by DSC. The observed higher curing degree in the subsequent DSC procedure underpins the incomplete curing of the samples during initial rheometry. Furthermore, the comparative assessment of the activation energies, molar ratios, and active groups of the two adhesives highlights the importance of the pre-exponential factor in addition to the activation energies, as it attributes to the probability of active groups coinciding at the appropriate spatial arrangement
Evaluation of a contactless accelerometer sensor system for heart rate monitoring during sleep
No full textThe monitoring of a patient's heart rate (HR) is critical in the diagnosis of diseases. In the detection of sleep disorders, it also plays an important role. Several techniques have been proposed, including using sensors to record physiological signals that are automatically examined and analysed. This work aims to evaluate using a contactless HR monitoring system based on an accelerometer sensor during sleep. For this purpose, the oscillations caused by chest movements during heart contractions are recorded by an installation mounted under the bed mattress. The processing algorithm presented in this paper filters the signals and determines the HR. As a result, an average error of about 5 bpm has been documented, i.e., the system can be considered to be used for the forecasted domain
Corporate startups: a systematic literature review on governance and autonomy
Get PDFMany incumbents observe the startup world in jealousy of their agility and innovational performance. An increasing number of initiatives aim to mimic startup-like procedures in order to increase the incumbents’ innovational output. Structural models like accelerators, spinoffs, incubators, or corporate venture capitals aim to achieve that goal by implementing different governance setups. However, the success of such initiatives often remains unclear. While there is broad research on such topics, a clear empirical view on governance mechanisms for entrepreneurial structures in incumbents is missing. This paper outlines how to build a governance model based on empirically validated mechanisms and their relationship to corporate startup autonomy. This is achieved by following the systematic literature review approach by Webster and Watson combined with qualitative data analysis techniques. The results describe relevant gaps in current research and identify promising pathways for future research
Assessing the relevance of different proximity dimensions for knowledge exchange and (co‑)creation in sustainability‑oriented innovation networks
Get PDFInnovations incorporating environmental and social considerations can address many sustainability challenges. Such sustainable innovations emerge in networks often comprising actors from business, academia, civil society, and government. The crucial interactions here are the (co-)creation and transfer of knowledge, mutual learning, and experimentation in different environments. To better understand these knowledge processes and hence the eventual outcome of sustainable innovations, we analyze the actors’ relationships with the help of proximity and its five dimensions, namely geographical, cognitive, institutional, organizational, and social proximity. Building upon findings from sustainability science and innovation system theory, we present a refined proximity framework, introducing a differentiation of institutional proximity into micro- and macro-institutional proximity and a differentiation of cognitive proximity into systems-cognitive, normative-cognitive, and transformative-cognitive proximity. Analyzing examples from the literature by applying this framework, we see that all proximity dimensions and their interdependencies help to better understand knowledge processes and innovations in sustainability-oriented innovation networks. We find that such networks often depict low levels of micro-institutional and systems-cognitive proximity, which coheres with the prevalence of inter- and transdisciplinary approaches and the wide inclusion of relevant stakeholders for addressing sustainability issues. Our framework further reveals that successful networks show high levels in other proximity dimensions, with normative-cognitive proximity appearing to play a crucial role, highlighting the importance of shared goal orientations. Our results provide valuable input for the formation of sustainability-oriented innovation networks by pointing out the necessary combination of distances that allow for creativity and learning, combined with appropriate proximities for exchange and mutual understanding
Explainable hybrid vision transformers and convolutional network for multimodal glioma segmentation in brain MRI
Get PDFAccurate localization of gliomas, the most common malignant primary brain cancer, and its different sub-region from multimodal magnetic resonance imaging (MRI) volumes are highly important for interventional procedures. Recently, deep learning models have been applied widely to assist automatic lesion segmentation tasks for neurosurgical interventions. However, these models are often complex and represented as “black box” models which limit their applicability in clinical practice. This article introduces new hybrid vision Transformers and convolutional neural networks for accurate and robust glioma segmentation in Brain MRI scans. Our proposed method, TransXAI, provides surgeon-understandable heatmaps to make the neural networks transparent. TransXAI employs a post-hoc explanation technique that provides visual interpretation after the brain tumor localization is made without any network architecture modifications or accuracy tradeoffs. Our experimental findings showed that TransXAI achieves competitive performance in extracting both local and global contexts in addition to generating explainable saliency maps to help understand the prediction of the deep network. Further, visualization maps are obtained to realize the flow of information in the internal layers of the encoder-decoder network and understand the contribution of MRI modalities in the final prediction. The explainability process could provide medical professionals with additional information about the tumor segmentation results and therefore aid in understanding how the deep learning model is capable of processing MRI data successfully. Thus, it enables the physicians’ trust in such deep learning systems towards applying them clinically