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Posits Asian countries have to redefine their competitive strategies and adjust their industries to the new situation that is happening in Europe. States Asian companies in the European climate require a new brand management approach, whereas in the USA 60 per cent of imported consumer hard goods come from China, but reckons this over‐dependence is dangerous and this is why Chinese companies are targeting Europe.

The paper aims to test the impact of gender‐relevant factors on professional respect for leaders.

Three determinants were analysed: gender constellation (gender match) between leaders and followers, gender‐stereotypic leadership behaviour, and followers' gender role attitudes. A field study with N₁=121 followers and their N₂=81 direct leaders from 34 German organisations was conducted. Leaders were on the lowest level of hierarchy.

The data showed that female leaders are at risk of receiving less professional respect from their followers than male leaders: male followers of female leaders had less professional respect than female followers of male leaders. Moreover, gender role discrepant female leaders (i.e. autocratic) got less respect than gender role discrepant male leaders (i.e. democratic). But no difference was found with regard to gender role congruent female (i.e. democratic) and male (i.e. autocratic) leaders. Finally, followers with traditional gender role attitudes were prone to have comparatively little professional respect for female leaders.

Future research should analyse gender‐relevant factors that influence the granting of professional respect and systematically compare these effects across branches. Furthermore, it would be interesting to see whether followers evaluate leaders from higher levels of hierarchy in the same way as our respondents did.

In order to promote women in leadership positions, followers' prejudices against female leaders should be reduced.

Field studies about the evaluation of female and male leaders explicitly considering their followers' gender role attitudes are rare. The results reflect that sexism is well and alive.

Meta‐analytic evidence exists that the numerical dominance of one gender group among employees can affect the behaviour of female and male leaders. The purpose of this paper is to hypothesis that leaders will show more transformational behaviour when they hold a minority status. Transformational behaviour might help to mitigate discrepancies between male leaders' gender and the feminine context, as well as between female leaders' gender and the masculine leadership role.

N₁=455 team members answered questionnaires about their work satisfaction and their team leaders' transformational leadership, whilst N₂=142 team leaders answered questions regarding their teams' goal fulfillment.

Female and male leaders are rated more transformational in economic sectors and working groups where they hold a minority status. The paper finds a positive interrelation between transformational leadership and followers' work satisfaction for male leaders, but not for female leaders.

Future research should compare female and male leaders from extremely gender‐typed economic sectors and from higher levels of the organisational hierarchy. This would provide evidence whether the findings could be generalised to other samples.

The findings point to the potential advantage of being a high‐transformational male leader in female‐dominated contexts. Irrespective of the numerical dominance of one gender group, followers of low‐transformational female leaders are more satisfied than those of low‐transformational male leaders.

The paper uses sector‐level (gender‐typicality of economic sectors) as well as group‐level data (gender‐composition of working groups) to account for the numerical dominance of female and male employees.

Histopathology biopsy imaging is currently the gold standard for the diagnosis of breast cancer in clinical practice. Pathologists examine the images at various magnifications to identify the type of tumor because if only one magnification is taken into account, the decision may not be accurate. This study explores the performance of transfer learning and late fusion to construct multi-scale ensembles that fuse different magnification-specific deep learning models for the binary classification of breast tumor slides.

Three pretrained deep learning techniques (DenseNet 201, MobileNet v2 and Inception v3) were used to classify breast tumor images over the four magnification factors of the Breast Cancer Histopathological Image Classification dataset (40×, 100×, 200× and 400×). To fuse the predictions of the models trained on different magnification factors, different aggregators were used, including weighted voting and seven meta-classifiers trained on slide predictions using class labels and the probabilities assigned to each class. The best cluster of the outperforming models was chosen using the Scott–Knott statistical test, and the top models were ranked using the Borda count voting system.

This study recommends the use of transfer learning and late fusion for histopathological breast cancer image classification by constructing multi-magnification ensembles because they perform better than models trained on each magnification separately.

The best multi-scale ensembles outperformed state-of-the-art integrated models and achieved an accuracy mean value of 98.82 per cent, precision of 98.46 per cent, recall of 100 per cent and F1-score of 99.20 per cent.

Getting high-quality data by removing the noisy data from the user-generated content (UGC) is the first step toward data mining and effective decision-making based on ubiquitous and unstructured social media data. This paper aims to design a framework for revoking noisy data from UGC.

In this paper, the authors consider a classification-based framework to remove the noise from the unstructured UGC in social media community. They treat the noise as the concerned topic non-relevant messages and apply a text classification-based approach to remove the noise. They introduce a domain lexicon to help identify the concerned topic from noise and compare the performance of several classification algorithms combined with different feature selection methods.

Experimental results based on a Chinese stock forum show that 84.9 per cent of all the noise data from the UGC could be removed with little valuable information loss. The support vector machines classifier combined with information gain feature extraction model is the best choice for this system. With longer messages getting better classification performance, it has been found that the length of messages affects the system performance.

The proposed method could be used for preprocessing in text mining and new knowledge discovery from the big data.

Like other European countries, Germany is facing regional physician shortages, which have several consequences on patient care. This study analyzes how hospitals perceive physician shortages and which strategies they adopt to address them. As a theoretical framework, the resource dependency theory is chosen.

The authors conducted 20 semi-structured expert interviews with human resource officers, human resource directors, and executive directors from hospitals in the northwest of Germany. Hospitals of different ownership types, of varying sizes and from rural and urban locations were included in the sample. The interviews were analyzed by using qualitative content analysis.

The interviewees reported that human resource departments in hospitals expand their recruiting activities and no longer rely on one single recruiting instrument. In addition, they try to adapt their retaining measures to physicians' needs and offer a broad range of employment benefits (e.g. childcare) to increase attractiveness. The study also reveals that interviewees from small and rural hospitals report more difficulties with attracting new staff and therefore focus on recruiting physicians from abroad.

Since the staffing situation in German hospitals will not change in the short term, the study provides suggestions for hospital managers and health policy decision-makers in dealing with physician shortages.

This study uses the resource dependency theory to explain hospitals' strategies for dealing with healthcare staff shortages for the first time.