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This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Practitioners should develop argumentation strategies based on contextual dependencies and the optimal strategy that aligns with the political cause.

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Managerial discourses tend to portray work-related mobility practices in a positive light, presenting mobility assignments as a place of stimulus and differentiation. A conception of mobility as an opportunity, may contrast, in specific economies and business settings, with lived personal experiences. This article reports the results of a three-year study, aimed to question how multinational companies (MNCs) located in a small and developing European economy (Portugal) are building talent pools for expatriate assignments. Interaction effects, as proposed by the job demands-resources (JD-R) theory, are considered as lens to understand the interplay of company expatriate policies, willingness profiles and psychological contracts of expatriates. By using a Portuguese sample, the study examines whether prior findings in mature economies and consolidated MNCs can be generalized to less developed international business settings.

A three-year study, encompassing 24 expatriate cases observed in five multinational firms born or located in Portugal. Two techniques of empirical data collection were used: statistical sources and documental analysis and in-depth interviews. A total of 37 interviews were conducted, both in-person and remotely, of which 13 were with company managers and representatives, and 24 with expatriates (as defined and referred like this by the companies under study).

Heterogeneous company policies, ranging from juvenile, functionalist to more dynamic and flow-based approaches, are presented as qualifying resources of willingness levels and psychological contracts of expatriates. Observed interaction effects between policies, willingness and psychological contracts, empirically mirrored in three profiles (conformist, protean and disrupted expatriates) suggest that incentive effects (emanating from company policies) and job demand-resource balance, factored as terms of social and economic trade, are non-linear and asymmetric, influencing firm propensity to succeed while using international work to support company expansion goals. As job resources, expatriate policies are presented as operating as pull or push factors: functionalist HR approaches seem to act as push factors generating more conformist or compelled willingness profiles.

Generalization of study's outcomes has limitations. Future studies are encouraged to use comparative and longitudinal research designs. Furthermore, future research should include business expatriates with entry-level positions, and increase the number of interviewees, as results can also be considered as limited by sample size.

It is suggested that further strategic work is needed to present expatriation development value, formally screen and consider willingness level as selection criteria, and enlarge the pool (from internal to external) of candidates, in peripheral economic settings such as Portugal. A shift to more dynamic and job resource-dense policies are suggested as beneficial, as pathway to optimize social and economic value from expatriation assignments and work experiences.

By putting the interplay between macro and micro-level processes into perspective, the study provides empirical evidence on how company expatriate policies have come to promote unforeseen differentiation of employee willingness and psychological contracts at the heart of MNCs. This is particularly relevant in developing economies such as Portugal, challenging the need to build talent pools for international work assignments. Empirical data illustrating company policies interactive effects with different willingness profiles and psychological contracts of expatriates is provided.

This paper addresses the emergence of strategic agility as adaptive capability to face the need to adapt to a constantly changing environment. Special emphasis is paid to governance mechanisms as a microfoundation of dynamic capabilities, investigating the dynamics and outcome in regards to the digitalization of supply chains and its socio-economic relations of its members.

A multiple-case study method is applied to explain the impact of a digitalization initiative in the form of an I4.0 implementation in a supply chain context. Building on qualitative in-depth interviews of experts in managerial positions, 16 cases from the manufacturing industry involving an I4.0 implementation across the supply chain were analyzed. Based on a conceptual design science approach, the CIMO logic (context, intervention, mechanisms and outcome) framework is used to investigate the interplay between agility as a dynamic capability and governance mechanisms.

The studied cases demonstrate an intensification of synergistic combinations of inter-firm resources to manage digitalization across the supply chain (SC). With the implementation of I4.0, competitive advantage along the SC takes higher priority over the competitive advantage in the individual or dyadic perspective. Strategic agility as a dynamic capability to cope with and adapt to the changing environment is hereby crucial emphasizing relational SC governance (SCG) as essential as a microfoundation to adjust the structure and management of SCs. Relationships, which allow competitive advantage as a SC balance out traditional power mechanisms within the SC hierarchy. Trust, collaboration and flexibility, aligned with common incentives in generating competitive advantages enable the SCs to mitigate uncertainty and risk while preventing opportunistic behavior.

The study offers SCM managers in-depth insights into strategic agility and how to address the recent challenge of digitalization successfully. Identifying crucial dynamics within SCG mechanisms based on first-hand use cases, practical success factors for implementing I4.0 to optimize effectiveness and efficiency can be derived how to structure and manage future SCs.

This paper contributes to the first insights examining the implications of digitalization and the role of strategic agility as a capability to adapt. Investigating the microfoundation of governance within this capability, this research provides insights on the socio-economic level, while discussing specific mechanisms how relationships of SCs evolve proposing relevant insights for future research.

The purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Typical convolutional neural networks (CNNs) are unable to capture both local and global contextual information effectively and apply a uniform operation to all pixels in an image. To address this, we propose an innovative approach that integrates a dynamic convolution operation at the encoder stage, improving image encoding quality and disease detection. In addition, a decoder based on the gated recurrent unit (GRU) is used for language modeling, and an attention network is incorporated to enhance consistency. This novel combination allows for improved feature extraction, mimicking the expertise of radiologists by selectively focusing on important areas and producing coherent captions with valuable clinical information.

In this study, we have presented a new report generation approach that utilizes dynamic convolution applied Resnet-101 (DyCNN) as an encoder (Verelst and Tuytelaars, 2019) and GRU as a decoder (Dey and Salemt, 2017; Pan et al., 2020), along with an attention network (see Figure 1). This integration innovatively extends the capabilities of image encoding and sequential caption generation, representing a shift from conventional CNN architectures. With its ability to dynamically adapt receptive fields, the DyCNN excels at capturing features of varying scales within the CXR images. This dynamic adaptability significantly enhances the granularity of feature extraction, enabling precise representation of localized abnormalities and structural intricacies. By incorporating this flexibility into the encoding process, our model can distil meaningful and contextually rich features from the radiographic data. While the attention mechanism enables the model to selectively focus on different regions of the image during caption generation. The attention mechanism enhances the report generation process by allowing the model to assign different importance weights to different regions of the image, mimicking human perception. In parallel, the GRU-based decoder adds a critical dimension to the process by ensuring a smooth, sequential generation of captions.

The findings of this study highlight the significant advancements achieved in chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Experiments conducted using the IU-Chest X-ray datasets showed that the proposed model outperformed other state-of-the-art approaches. The model achieved notable scores, including a BLEU_1 score of 0.591, a BLEU_2 score of 0.347, a BLEU_3 score of 0.277 and a BLEU_4 score of 0.155. These results highlight the efficiency and efficacy of the model in producing precise radiology reports, enhancing image interpretation and clinical decision-making.

This work is the first of its kind, which employs DyCNN as an encoder to extract features from CXR images. In addition, GRU as the decoder for language modeling was utilized and the attention mechanisms into the model architecture were incorporated.

This paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained knowledge element perspective. This endeavor seeks to unlock the latent value embedded within newspaper contents while simultaneously furnishing invaluable guidance within methodological paradigms for research in the humanities domain.

According to the semantic organization process and knowledge element concept, this study proposes a holistic framework, including four pivotal stages: knowledge element description, extraction, association and application. Initially, a semantic description model dedicated to knowledge elements is devised. Subsequently, harnessing the advanced deep learning techniques, the study delves into the realm of entity recognition and relationship extraction. These techniques are instrumental in identifying entities within the historical newspaper contents and capturing the interdependencies that exist among them. Finally, an online platform based on Flask is developed to enable the recognition of entities and relationships within historical newspapers.

This article utilized the Shengjing Times·Changchun Compilation as the datasets for describing, extracting, associating and applying newspapers contents. Regarding knowledge element extraction, the BERT + BS consistently outperforms Bi-LSTM, CRF++ and even BERT in terms of Recall and F1 scores, making it a favorable choice for entity recognition in this context. Particularly noteworthy is the Bi-LSTM-Pro model, which stands out with the highest scores across all metrics, notably achieving an exceptional F1 score in knowledge element relationship recognition.

Historical newspapers transcend their status as mere artifacts, evolving into invaluable reservoirs safeguarding the societal and historical memory. Through semantic organization from a fine-grained knowledge element perspective, it can facilitate semantic retrieval, semantic association, information visualization and knowledge discovery services for historical newspapers. In practice, it can empower researchers to unearth profound insights within the historical and cultural context, broadening the landscape of digital humanities research and practical applications.

The introduction of robots as value-adding “workers” on the shop floor triggers complex changes to manufacturing work. Such changes involve highly entangled relationships between technology, organisation and people. Understanding such entanglements requires a holistic assessment of contemporary robotised manufacturing work, to anticipate the dynamically emerging opportunities and risks of robotised work.

A systematic literature review of 87 papers was conducted to capture relevant themes of change in robotised manufacturing work. The literature was analysed using a thematic analysis approach, with Checkland’s soft systems thinking as an analytical framework.

Based on the literature analysis, the authors present a systemic conceptualisation of robotised manufacturing work. Specifically, the conceptualisation highlights four entangled themes of change: work, organisation of labour, workers’ (experiences) and the firm’s environment. Moreover, the authors discuss the complex patterns of interactions between these objects as relationships that defy straightforward cause–effect models.

The findings draw attention to complex interactions between robotisation and manufacturing work. It can, therefore, inform strategic decisions and support projects for robotisation from a holistic perspective.

The authors present a novel approach to studying and designing robotised manufacturing work as a conceptual system. In particular, the paper shifts the focus towards crucial properties of the system, which are subject to complex changes alongside the introduction of robot technology in manufacturing. Soft systems thinking enables new research avenues to explain complex phenomena at the intersection of robotisation and manufacturing work.

This paper develops a typology of argumentation strategies used in lobbying. Unlike in other strategic communication functions such as crisis or risk communication, such typologies have not been proposed in the sub-field of public affairs.

The article synthesises the strategic communication, political communication and policy studies literature and employs exchange theory to explain the communicative-strategic exchange in public affairs. It showcases its explanatory potential with illustrative examples from Big Tech lobbying.

The paper describes that categories of argumentation strategies that a public affairs professional will choose are based on the contingency of the issue, policy objective and lobbying objective. The descriptive typology will require empirical testing to develop further.

The paper describes how public affairs professionals influence public policy through their argumentation strategies, which sheds light on the usually opaque activities of lobbying.

The proposed typology is the first of its kind for the field of public affairs. Beyond, it contributes communication-scientific insights from a rhetorical tradition to strategic communication research and other social science fields where lobbying is studied, e.g. policy studies.

Tourists with different cultural backgrounds think and behave differently. Accurately capturing and correctly understanding cultural differences will help tourist destinations in product/service planning, marketing communication and attracting and retaining tourists. This research employs Hofstede's cultural dimensions theory to analyse the variations in destination image perceptions of Chinese-speaking and English-speaking tourists to Xiamen, a prominent tourist attraction in China.

The evaluation utilizes a two-stage approach, incorporating LDA and BERT-BILSTM models. By leveraging text mining, sentiment analysis and t-tests, this research investigates the variations in tourists' perceptions of Xiamen across different cultures.

The results reveal that cultural disparities significantly impact tourists' perceived image of Xiamen, particularly regarding their preferences for renowned tourist destinations and the factors influencing their travel experience.

This research pioneers applying natural language processing methods and machine learning techniques to affirm the substantial differences in the perceptions of tourist destinations among Chinese-speaking and English-speaking tourists based on Hofstede's cultural theory. The findings furnish theoretical insights for destination marketing organizations to target diverse cultural tourists through precise marketing strategies and illuminate the practical application of Hofstede's cultural theory in tourism and hospitality.

This paper aims to offer a comprehensive examination of the various solutions currently accessible for addressing the challenge of semantic interoperability in cyber physical systems (CPS). CPS is a new generation of systems composed of physical assets with computation capabilities, connected with software systems in a network, exchanging data collected from the physical asset, models (physics-based, data-driven, . . .) and services (reconfiguration, monitoring, . . .). The physical asset and its software system are connected, and they exchange data to be interpreted in a certain context. The heterogeneous nature of the collected data together with different types of models rise interoperability problems. Modeling the digital space of the CPS and integrating information models that support cyber physical interoperability together are required.

This paper aims to identify the most relevant points in the development of semantic models and machine learning solutions to the interoperability problem, and how these solutions are implemented in CPS. The research analyzes recent papers related to the topic of semantic interoperability in Industry 4.0 (I4.0) systems.

Semantic models are key enabler technologies that provide a common understanding of data, and they can be used to solve interoperability problems in Industry by using a common vocabulary when defining these models.

This paper provides an overview of the different available solutions to the semantic interoperability problem in CPS.

This research investigates performance determinants of service sector foreign-owned subsidiaries located in an emerging market. The focus is on the two dimensions of organizational networks (Who do you know?) and competencies (What do you know?).

Data were collected via a large-scale survey of managing directors located in the midrange emerging economy of Taiwan. The data are analyzed using partial least squares structured equation modeling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA) techniques.

The results show the importance of intraorganizational network strength as a key determinant of subsidiary performance, and that combinations of interorganizational network strength and competencies can determine performance in several subsidiaries.

This article offers new insights by testing a theoretical framework based on network perspective and the competence-based view of the firm in an emerging market context. It also offers an additional twist by employing symmetric (PLS-SEM) and nonsymmetric (fsQCA) methods to test the framework. This allows to arrive at robust conclusions about the complementarity and substitutability of the applied theories. This research also contributes to the current literature by providing fine-grained insights into the nature and impact of competencies and networks. It is also one of the few studies to focus specifically on service sector subsidiaries.