The field of strategic management has witnessed a proliferation of theoretical perspectives following two main paradigms. The two camps roughly correspond, on the one hand, to the "classical" and "neo-classical" approaches, and on the other to the "contingency", "post-classical" and "postmodern" approaches. Underpinning these two camps are different conceptions of the theory of the firm and the purpose of theory itself in understanding business strategy, which are difficult and perhaps impossible to reconcile. Against this backdrop, this paper seeks to expose the roots of this interminable argument over theory and proposes a way to escape this situation of incommensurability.
Entrepreneurs need to mobilize funds, but they do so under considerable uncertainty about resource holders’ preferences, leading often to fundraising attempts that perform below entrepreneurs’ aspirations. Past research has offered contrasting theorizing and evidence for why entrepreneurs then make changes to their product offering during such attempts as well as for why entrepreneurs refrain from taking such action. This paper develops and tests behavioral theory to reconcile this tension, explicating when and why entrepreneurs change their product offering during underperforming fundraising attempts. Specifically, we argue that entrepreneurs draw on three sources of information that are inherent to fundraising attempts and that inform the extent of their actions to change their product offering: the degree to which they perform below their own fundraising aspirations, the degree to which they fall below peer fundraising performance, and the time that remains until the deadline for the fundraising attempt. Longitudinal data on 576 fundraising campaigns (6,758 observations) published on the crowdfunding platform Kickstarter support our theory. By developing novel behavioral theory on when and why entrepreneurs take action during resource mobilization, we offer contributions to research on entrepreneurial resource mobilization, the crowdfunding literature, and the Behavioral Theory of the Firm. Funding: This work was supported by KölnAlumni. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2020.13803 .
Proton exchange membrane fuel cells (PEMFC) have been noticed by researchers due to their high efficiency, low pollution, and high‐power density in distributed generation systems. In this paper, an LCL‐type grid‐tied PEMFC fuel cell power conditioning system is evaluated in a harmonics‐polluted low‐voltage grid. The LCL‐filters can lead to resonance and instability despite their capability to attenuate harmonics. In this research, a transformer has been used to connect the fuel cell inverter to the grid. The grid‐side inductor of LCL‐filter is realized by the leakage inductance of the transformer. In addition, for more effective resonance damping and attenuation of current ripples caused by the grid voltage harmonics, a capacitor voltage comprehensive feedback control has been designed and investigated. The comprehensive feedback control of the capacitor voltage contains proportional, first and second‐order derivative terms. In the proposed control scheme, the capacitor‐current‐feedback is opposed by the capacitor voltage derivative term due to reverse loop gain, which leads to deleting both of these loop gains. As a result, there is no need to utilize a current sensor in this control method. Consequently, the proportional and second‐order derivative terms of the capacitor voltage attenuate the LCL‐filter resonance. A low‐pass filter is also considered in the second‐order derivative loop in the controllable frequency range to ensure system stability. The simulation results of the PEMFC power conditioning system in different conditions confirm the proper attenuation of LCL resonance of grid‐tied inverter, high‐quality current injection to the harmonics polluted grid, the suitable stability, and the appropriate dynamic response for the proposed system. Under the proposed control scheme, the fuel cell power conditioning system demonstrates satisfactory stability. Even when reducing the LCL filter values by 5%–20%, the system maintains its stability effectively. Moreover, the THD of the injected current into the grid, employing the proposed control strategy, has been successfully reduced to an impressive value of 1.97% in a weak and harmonical grid.
By presenting an investigation of the impact of international trade protectionism on the reconfigurations of the global value chains (GVCs), this paper challenges the perceived assumption of ongoing globalization and the free flow of goods and services. Building on the de-globalization and GVCs’ literature, we performed a historical content analysis on 174 articles from 2016 to 2020 published in leading and major national and international newspapers. Our findings suggest that international trade protectionism has altered the landscape of GVCs by causing widespread disruption to their functioning, thus making them prone to future external policy risks. Such disruption is having a varying impact on various industries, whereby it is causing greater harm to those industries that are more global in nature and thus rely on global suppliers. We draw implications of our findings for research and practice.
Introduction Particularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve the accuracy and efficiency of skin lesion analysis, CAD systems play a crucial role. To segment and classify skin lesions from dermoscopy images, this study focuses on using hybrid deep learning techniques. Method This research uses a hybrid deep learning model that combines two cutting-edge approaches: Mask Region-based Convolutional Neural Network (MRCNN) for semantic segmentation and ResNet50 for lesion detection. To pinpoint the precise location of a skin lesion, the MRCNN is used for border delineation. We amass a huge, annotated collection of dermoscopy images for thorough model training. The hybrid deep learning model to capture subtle representations of the images is trained from start to finish using this dataset. Results The experimental results using dermoscopy images show that the suggested hybrid method outperforms the current state-of-the-art methods. The model's capacity to segment lesions into distinct groups is demonstrated by a segmentation accuracy measurement of 95.49 percent. In addition, the classification of skin lesions shows great accuracy and dependability, which is a notable advancement over traditional methods. The model is put through its paces on the ISIC 2020 Challenge dataset, scoring a perfect 96.75% accuracy. Compared to current best practices in IoMT, segmentation and classification models perform exceptionally well. Conclusion In conclusion, this paper's hybrid deep learning strategy is highly effective in skin lesion segmentation and classification. The results show that the model has the potential to improve diagnostic accuracy in the setting of IoMT, and it outperforms the current gold standards. The excellent results obtained on the ISIC 2020 Challenge dataset further confirm the viability and superiority of the suggested methodology for skin lesion analysis.
Efficiently extracting features from satellite images is crucial for classification and post-processing activities. Many feature representation models have been created for this purpose. However, most of them either increase computational complexity or decrease classification efficiency. The proposed model in this paper initially collects a set of available satellite images and represents them via a hybrid of long short-term memory (LSTM) and gated recurrent unit (GRU) features. These features are processed via an iterative genetic algorithm, identifying optimal augmentation methods for the extracted feature sets. To analyse the efficiency of this optimization process, we model an iterative fitness function that assists in incrementally improving the classification process. The fitness function uses an accuracy & precision-based feedback mechanism, which helps in tuning the hyperparameters of the proposed LSTM & GRU feature extraction process. The suggested model used 100 k images, 60% allocated for training and 20% each designated for validation and testing purposes. The proposed model can increase classification precision by 16.1% and accuracy by 17.1% compared to conventional augmentation strategies. The model also showcased incremental accuracy enhancements for an increasing number of training image sets.
We examine financial literacy in Finland and its connection with various financial outcomes using novel survey data collected in 2023. While the overall Finnish financial literacy level is about average among the OECD countries, there is significant heterogeneity within the population. Women have lower financial literacy than men. The young and the old have lower financial literacy than respondents in their prime working age, and entrepreneurs have higher financial literacy than other groups. Financial literacy is also correlated with higher educational levels. We further study the relationship between financial literacy and a number of economic outcome variables. We find financial literacy to be negatively related to coping with a major expense, facing an income shock, and with perceived over-indebtedness. However, we do not find a statistically significant relationship between financial literacy and retirement planning in Finland.
Automatic classification of Lyme disease rashes on the skin helps clinicians and derma-tologists' probe and investigate Lyme skin rashes effectively. This paper proposes a new in-depth features fusion system to classify Lyme disease rashes. The proposed method consists of two main steps. First, three different deep learning models, Densenet201, InceptionV3, and Exception, were trained independently to extract the deep features from the erythema migrans (EM) images. Second, a deep feature fusion mechanism (meta classifier) is developed to integrate the deep features before the final classification output layer. The meta classifier is a basic deep convolutional neural network trained on original images and features extracted from base level three deep learning models. In the feature fusion mechanism, the last three layers of base models are dropped out and connected to the meta classifier. The proposed deep feature fusion method significantly improved the classification process, where the classification accuracy was 98.97%, which is particularly impressive than the other state-of-the-art models.
An increasing number of studies have been conducted on children with early life international mobility, that is, on third culture kids (TCKs). In this chapter, we review how TCKs appear in the existing research through the TCKs’ migration journey. We include TCK research that is reported within different disciplines to form a wide perspective on the experiences of this population. We cover different stages of the mobility from pre-departure preparation to cross-cultural adjustment, and further on to the repatriation stage. Finally, we discuss the longer-term challenges and benefits of being an adult third culture kid (ATCK). The review indicates that the time spent abroad has a significant impact on the ATCK’s worldview, education decisions, identity, and future career. It thus highlights the importance of supporting TCKs in the different stages of mobility, despite such practices often still seeming to be overlooked. As an outcome of the review, a future research agenda is suggested to help improve our understanding of TCKs’ international experiences, and the long-term impacts they have on the later life and career of ATCKs. As an implication, it is raised that TCKs offer an attractive pool of employees for companies looking for staff with international competencies.
Existing literature and research on the career impacts of expatriation have focused on expatriates sent abroad by their employers. In turn, the career experiences of other types of global workers (e.g., migrants and self-initiate expatriates) are more limited in number. However, a growing number of individuals self-initiate their international professional journey and search for a job abroad without organizational support. Such experiences often offer similar developmental opportunities as expatriation within companies, but our evidence is still limited. Due to their developed international competencies, self-initiated expatriates (SIE) represent an attractive global staffing alternative for organizations assuming that they understand how expatriation develops individuals’ competencies. Based on an internet survey and interviews with SIE members of two Finnish trade associations, this chapter investigates the career capital (CC) development of highly skilled Finnish SIEs. The two associations identified individuals working abroad in 2015 and 2016, and they received our questionnaire in 2020. The present study’s results support the view that SIEs generally develop their CC fairly extensively when working abroad. This view contrasts with previous studies that emphasize more negative views on the development of SIEs, who are described as facing challenges in finding suitable jobs abroad that fit their level of education and CC. Therefore, the context in which the SIEs work may impact their learning opportunities and perception of the value of such international work experience.
We are living in the digital era, in which firms often face many challenges due to the rapid development of digital technologies. Thus, firms need to reform their traditional business models by integrating digital technologies into all areas of existing business processes for their survival. This integration process is called digital transformation (DT). However, the understanding of how to develop a sustainable DT process for firms remains incomplete and fragmented. As a result, we studied how DT unfolds over a period of years in the case of telecenters (TCTs) in the context of sustainability. We used a qualitative case study as our research approach. We contribute to the literature by introducing a model of a digital transformation process and its relationship with sustainability. We also contribute to practice by suggesting that in order to ensure sustainability for the long term, managers need to prioritize the sustainable factors in each phase of the DT process while maintaining, continuously seeking, and implementing new digital initiatives.
In rapid development, Selective Laser Sintering (SLS) creates prototypes by processing industrial materials, for example, polymers. Such materials are usually in powder form and fused by a laser beam. The manufacturing quality depends on the interaction between a high‐energy laser beam and the powdered material. However, in‐homogeneous temperature distribution, unstable laser powder, and inconsistent powder densities can cause defects in the final product, for example, Powder Bed Defects. Such factors can lead to irregularities, for example, warping, distortion, and inadequate powder bed fusion. These irregularities may affect the profitable SLS production. Consequently, detecting powder bed defects requires automation. An ensemble learning‐based approach is proposed for detecting defects in SLS powder bed images from this perceptive. The proposed approach first pre‐processes the images to reduce the computational complexity. Then, the Convolutional Neural Network (CNN) based ensembled models (off‐the‐shelf CNN, bagged CNN, and boosted CNN) are implemented and compared. The ensemble learning CNN (bagged and boosted CNN) is good for powder bed detection. The evaluation results indicate that the performance of bagged CNN is significant. It also indicates that preprocessing of the images, mainly cropping to the region of interest, improves the performance of the proposed approach. The training and testing accuracy of the bagged CNN is 96.1% and 95.1%, respectively.
Osteoporosis is a skeletal disease that is commonly seen in older people but often neglected due to its silent nature. To overcome the issue of osteoporosis in men and women, we proposed an advanced prediction model with the help of machine learning techniques which can help to identify the potential occurrence of this bone disease by its advanced screening tools. To achieve more reliable and accurate results, various machine‐learning techniques were applied to the presented data sets. Moreover, we also compared the performance of our results with other existing algorithms to solely focus on the advanced features of the proposed methodology. The two data sets, the clinical tests of patients in Taiwan and medical reports of postmenopausal women in Korea through Korean Health and Nutrition Examination Surveys (2010–2011) were considered in this study. To predict bone disorders, we utilized the data about females and developed a system using artificial neural networks, support vector machines, and K‐nearest neighbor. To compare the performance of the model Area under the Receiver Operating Characteristic Curve and other evaluation metrics were compared. The achieved results from all the algorithms and compared them with Osteoporosis Self‐Assessment Tool for Asians and the results were noticeably better and more reliable than existing systems due to the involvement of ML. Using machine learning techniques to predict these types of diseases is better because physicians and patients can take early action to prevent the consequences in advance.
Background We investigated whether a short, 5-min magnetic resonance imaging (MRI) protocol consisting of only axial T2-weighted and diffusion-weighted imaging (DWI) sequences can discriminate between tonsillar infections, peritonsillar abscesses and deeply extending abscesses in a retrospective, blinded, multireader setting. Methods We included patients sent by emergency physicians with suspected pharyngotonsillar infections who underwent emergency neck 3-T MRI from April 1 2013 to December 31 2018. Three radiologists (with 10−16 years of experience) reviewed the images for abscesses and their extension into deep neck spaces. Data were reviewed first using only axial T2-weighted Dixon images and DWI (short protocol) and second including other sequences and contrast-enhanced T1-weighted Dixon images (full protocol). Diagnostic accuracy, interobserver agreement, and reader confidence were measured. Surgical findings and clinical course served as standard of reference. Results The final sample consisted of 52 patients: 13 acute tonsillitis with no abscesses, 19 peritonsillar abscesses, and 20 deeply extending abscesses. Using the short protocol, diagnostic accuracy for abscesses across all readers was good-to-excellent: sensitivity 0.93 (95% confidence interval 0.87−0.97), specificity 0.85 (0.70−0.93), accuracy 0.91 (0.85−0.95). Using the full protocol, respective values were 0.98 (0.93−1.00), 0.85 (0.70−0.93), and 0.95 (0.90−0.97), not significantly different compared with the short protocol. Similar trends were seen with detecting deep extension. Interobserver agreement was similar between protocols. However, readers had higher confidence in diagnosing abscesses using the full protocol. Conclusions Short MRI protocol showed good-to-excellent accuracy for tonsillar abscesses. Contrast-enhanced images improved reader confidence but did not affect diagnostic accuracy or interobserver agreement. Relevance statement Short protocol consisting only of T2-weighted Dixon and DWI sequences can accurately image tonsillar abscesses, which may improve feasibility of emergency neck MRI. Key points • The short 3-T MRI protocol (T2-weighted images and DWI) was faster (5 min) than the full protocol including T1-weighted contrast-enhanced images (24 min). • The short 3-T MRI protocol showed good diagnostic accuracy for pharyngotonsillar abscesses. • Contrast-enhanced sequences improved reader confidence but did not impact diagnostic accuracy or interobserver agreement. Graphical Abstract
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