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In international business (IB), the discussion of COVID-19-related global value chain (GVC) models driving resilience has taken momentum since May 2020. The purpose of this study is to uncover insights that the pandemic provided as a unique research opportunity, holistically, revealing the significant role of non-lead firms in GVC outcomes and resilience. This allows to extend theory as the authors critically identify impact criteria and assess interdependence and valence, thus progressing the traditional (pre-pandemic) IB view of GVC governance and orchestration.

This study opts for an integrative review to help create a much-needed extension of IB theory by means of a critical perspective on GVC theory. The authors examine the extant body of IB literature as the relevant stock of collective IB knowledge prompted by the COVID-19 pandemic, uncovering contributions – with a focus on the role of non-lead firms in orchestration and resilience – that allows to clarify what was not evident pre-pandemic. With this, the authors move the theory from its efficiency focus to a better recognition of the interdependencies of power and profit outcomes stemming from asymmetries of interrelationships. By design, the authors focus on the unique research period of the pandemic and orchestration complexities along the development of configurational arguments beyond simple correlations (Fiss, 2011), revealing key dependencies as key themes. The authors highlight further research avenues following Snyder (2019) that are called upon to strengthen that understanding and that helps extend theory.

This research provides a critical perspective on the application of the traditional IB views for GVC governance (designed for efficiency, cost and proximity to markets with pre-dominance for just in time), which has shifted during the pandemic to accommodate for adaptation and adjustment to resilience and just in case considerations. The holistic review reveals not only the key country- and multinational enterprise (MNE)-dependencies with residual impact determining the balance between just-in-time and just-in-case. Also, the authors advance the understanding of the (un)balance of the traditional GVC – focused on just-in-case rather than just-in-time through a lead and non-lead GVC participation and power lens yet rarely observed. The authors find that governance should not be construed as “management” such that it resolves into decisions undertaken in lead firms for execution in subordinate GVC participants. Autonomy allows to subsidiary units by MNE lead firms and/or exercised by (mainly, innovative) non-subsidiary GVC participant firms, is uncovered as a key driver in this. Greater delegation capacity appears to help provide resilience to loss in profit, with a recognition that there may be a dynamic trade-off between power and profit. In addition, the authors are able to identify correlations with innovation, demand elasticity, digital uptake, investment and other, that the authors trust will set the scene for additional research deepening and extending the findings.

Integrative literature reviews include a problem formulation (i.e. that is limited to published topics around an emerging theme) and are hence very focused in nature and approach. This applies to this paper. Data analysis in this method is not typically using statistical methods in contrast to meta-analyses. Also, the authors limit the sample to a relatively short time period with 33 publications analysed, purposefully focusing on the most prompt and “acute” insights into GVCs during the pandemic.

The traditional GVC governance model is designed for efficiency, cost and proximity to markets with pre-dominance for just in time. The authors reveal dependencies that are instrumental to better understand lead and non-lead interaction and relative autonomy, with a focus on residual impact determining the balance between just-in-time and just-in-case that, if in the sought equilibrium and agile, can allow alignment with context and this resilience. This paper specifically provides practical insights and visualization that highlights stages/“ripple” effects and their impact and the questions to ask as stakeholders look for GVC resilience. This includes, int.al., firms and their role as strategic agents, prompting participants through the learnings from exogenous shock to realign their strategies, redistributed manufacturing of production across subsidiary and non-subsidiary non-lead firms, greater competition and hence power for suppliers leveraging resilience and innovation, greater understanding of localization and regionalization of production of essential supplies, interaction with governments, and of investment impacts abroad especially to secure GVC participation.

The insights provided through this extension of theory with its literature review reveal the importance of aligning IB research into GVCs to factors that became visible through alternative or unusual settings, as they have the power to reveal the limitations of traditional views. In this case, a mainly efficiency-led, just-in-time focused GVC governance model is reviewed through the literature that emanated during the pandemic, with a critical perspective, which helped uncover and underline the complexities and evolution of GVC governance, providing fundamental support to solutioning the continuing global supply chain challenges that started as a result of the pandemic and are yet again accelerated by the Ukraine and Middle Eastern wars and its impact with, int.al., concerns over possible severe global food, labour/migration and resources crises. IB holds a social responsibility to help identify critical challenges from the disciplinary perspective and help advance resilience for social benefit.

This paper supports the original IB theory development by extending GVC theory into the lead – non-lead dynamics that may, under certain conditions, provide a “Resilience wall” for GVCs. The value created through insights stemming from a unique period of time for GVC is significant. It allows us thus also to pave the way to an emerging and critical research adaption looking into equilibrium, nuancing demand elasticity, better understanding trade and investment impacts along GVCs and more. By examining views on the sources of pandemic risks in a possibly unique setting, the authors offer added value from extant IB research insights by combining them, revealing the importance for GVCs to investigate not only key dependencies between the exogenous shock, i.e. context, and the impacts assessed through this literature but to further use their inherent value to create a framework for further conceptualization and extension of the traditional IB view on GVC governance. This work illustrates the urgency and importance for IB to take a timely and possibly more critical approach to the investigation of governance models that have, to date, shown some significant limitations.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

This work analyzes female immigrants’ integration in the dimensions of education, labor market participation and fertility in 15 European countries, considering individual characteristics, including cultural background, host countries’ attitudes towards immigrants, the role of women in the family and country-specific integration policy. All these aspects taken together are crucial to understand the main patterns of integration focusing on gender differences.

We focus on second- and first-generation male and female immigrants between the age of 25 and 41, with a length of stay of at least ten years. Enrollment ratios for tertiary education in parents’ countries, the total fertility rate and the female labor force in the mother’s country represent ethnic background. Diversity in the destination regions is captured by local attitudes towards immigrants, the perceived role of women and national policies to integrate migrants [Migrant Integration Policy Index (MIPEX)]. The data are drawn from the European Social Survey (ESS) for 2010–2018. Our results are based on ordinary least squares (OLS) and logit estimates; multilevel analysis was conducted.

We find significant evidence of gender role transmission from mother to daughter; age at immigration seems to be crucial to examine the importance of the culture of origin among immigrants. However, females are responsive to attitudes toward immigrants and gender equality in receiving societies, while integration policies, by defining the set of opportunities, may contribute to both genders’ tertiary education and women’s probability of being in the labor force.

This work underlines that integration policies favoring equal rights as nationals may contribute to both women’s tertiary education and their probability of being in the labor force.

We explore female integration in Europe in the dimensions of education, labor market, fertility and the role of both immigrants’ cultural heritage and specific aspects of destination countries. Previous research, particularly in the USA, has generally focused on some of these features at the expense of a more comprehensive approach. This study builds upon the existing literature and contributes to it by taking a multifaceted approach to female integration in Western Europe, which presents not only an institutional context different from the USA but also some heterogeneity with respect to integration policies and socioeconomic factors.

This paper aims to examine the relationship between environmental, social and governance (ESG) performance and systemic risk sensitivity of Chinese listed companies. From the consumer loyalty and investor structure perspectives, the relationship between ESG performance and systemic risk sensitivity is analyzed.

Since Morgan Stanley Capital International (MSCI) ESG officially began to analyze and track China A-shares from 2018, 275 listed companies in the SynTao Green ESG testing list for 2015–2021 are selected as the initial model. To measure the systematic risk sensitivity, this study uses the beta coefficient, from capital asset pricing model (CPAM), employing statistics and data (STATA) software.

The study reveals that high ESG rating companies have high corresponding consumer loyalty and healthy trading structure of institutional investors, thereby the systemic risk sensitivity is lower. This paper reveals that companies with high ESG rating are significantly less sensitive to systemic risk than those with low ESG rating. At the same time, ESG has a weaker impact on the systemic risk of high-cap companies than low-cap companies.

The study helps the companies understand the influence of market value on the relationship between ESG performance and systemic risk sensitivity. Moreover, this paper explains explicitly why ESG performance insulates a firm’s stock from market downturns with the lens of consumer loyalty theory and investor structure theory.

The paper provides new insights on the company’s ESG performance that significantly affects the company’s systemic risk sensitivity.

Our study aims to evaluate the impact of infrastructure and public investment on private investment in machinery and equipment in Brazil from 1947 to 2017. The contribution of our article to the existing literature lies in providing a more comprehensive understanding of the presence or absence of the crowding effect in the Brazilian economy by leveraging an extensive historical database. Our central argument posits that the recent decline in private capital accumulation over the last few decades can be attributed to shifts in economic policies – moving from a developmentalist orientation to nondevelopmental guidance since the early 1990s, which is reflected in the diminished levels of public investment and infrastructure since the 1980s.

We conducted a series of econometric regressions utilizing the autoregressive distributed lag (ARDL) model as our chosen econometric methodology.

Employing two different variables to measure public investment and infrastructure, our results – robust across various specifications – have substantiated the existence of a crowding-in effect in Brazil over the examined period. Thus, we have empirical evidence indicating that the state has influenced private capital accumulation in the Brazilian economy over the past decades.

Our article contributes to the existing literature by offering a more comprehensive understanding of the crowding effect in the Brazilian economy, utilizing an extensive historical database.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

This paper explores the convergence of Education 4.0 and Industry 4.0 and presents a Twin Peaks model for their seamless integration.

A high-level literature review is conducted to identify and discuss the important challenges and opportunities offered by both Education 4.0 and Industry 4.0. A novel Twin Peaks model is devised for the convergence of these domains and to cope with the challenges effectively.

The proposed Twin Peak model for the convergence of Education 4.0 and Industry 4.0 suggests that the development of these two domains is interdependent. It emphasizes ethical considerations, inclusivity and understanding the concerns of stakeholders from both education and industry. We have also explained how continuous incremental adaptation within the proposed Twin Peaks model might assist in addressing concerns of one sector with the opportunities of the other.

First, Education 4.0 and Industry 4.0 are reviewed in terms of opportunities and challenges they present. Second, a novel Twin Peaks model for the convergence of Education 4.0 and Industry 4.0 is presented. The proposed discovers that the convergence is adaptive, iterative and must be ethically sound while considering the broader societal implications of the digital transformation. Third, this study also acts as a torch-bearer for the necessity for more research of this kind to guarantee that our educational ecosystem is adaptable and capable of producing the skills required for success in the era of IR4.0.

This study aims to investigate the effect of monetary policy on housing prices for US economy. It specifically examines whether nominal or real interest rates are the key drivers behind fluctuations in housing prices in US.

Monthly data from January 1991 to July 2023 and various appropriate analytical tools such as unit root tests, Johansen’s cointegration test, vector error correction model (VECM), impulse response function and Granger causality test were applied for the data analysis.

The Johansen cointegration findings reveal the presence of a long-term relationship among the variables. VECM results indicate a negative correlation between nominal and real interest rates and housing prices in both the short and long terms, suggesting that a strict monetary policy can help in controlling the housing price increase in the USA. However, housing prices are more responsive to changes in nominal interest rates than to real interest rates. Additionally, the study reveals that the COVID-19 pandemic contributed to the upsurge in housing prices in the USA.

This study contributes by examining the role that nominal or real interest rates play in shaping housing prices in the USA. Moreover, given the recent significant upsurge in housing prices, this study presents a unique opportunity to investigate whether these price increases are influenced by the Federal Reserve's monetary policy decisions regarding nominal or real interest rates. Additionally, using monthly data, this study provides a deeper understanding of the fluctuations in housing prices and their connection to monetary policy tools.

Because of the advantages of high deposition efficiency and low manufacturing cost compared with other additive technologies, robotic wire arc additive manufacturing (WAAM) technology has been widely applied for fabricating medium- to large-scale metallic components. The additive manufacturing (AM) method is a relatively complex process, which involves the workpiece modeling, conversion of the model file, slicing, path planning and so on. Then the structure is formed by the accumulated weld bead. However, the poor forming accuracy of WAAM usually leads to severe dimensional deviation between the as-built and the predesigned structures. This paper aims to propose a visual sensing technology and deep learning–assisted WAAM method for fabricating metallic structure, to simplify the complex WAAM process and improve the forming accuracy.

Instead of slicing of the workpiece modeling and generating all the welding torch paths in advance of the fabricating process, this method is carried out by adding the feature point regression branch into the Yolov5 algorithm, to detect the feature point from the images of the as-built structure. The coordinates of the feature points of each deposition layer can be calculated automatically. Then the welding torch trajectory for the next deposition layer is generated based on the position of feature point.

The mean average precision score of modified YOLOv5 detector is 99.5%. Two types of overhanging structures have been fabricated by the proposed method. The center contour error between the actual and theoretical is 0.56 and 0.27 mm in width direction, and 0.43 and 0.23 mm in height direction, respectively.

The fabrication of circular overhanging structures without using the complicate slicing strategy, turning table or other extra support verified the possibility of the robotic WAAM system with deep learning technology.

The purpose of this study is to assess the influence of institutional quality (IQ), fossil fuel efficiency, structural change and renewable energy (RE) consumption on carbon efficiency.

This research uses an econometric approach, more specifically the Autoregressive Distributed Lag model, to examine the relationship between structural change, RE consumption, IQ, fossil fuel efficiency and carbon efficiency in India from 1996 to 2019.

This study finds the positive contributions of variables like fossil fuel efficiency, technological advancement, structural transformation, IQ and increased RE consumption in fostering environmental development through enhanced carbon efficiency. Conversely, this study emphasises the negative contribution of trade openness on carbon efficiency. These findings provide concise insights into the dynamics of factors impacting carbon efficiency in India.

This study's exclusive focus on India limits the generalizability of findings. Future studies should include a broader range of variables impacting various nations' carbon efficiency. Furthermore, it is worth noting that this study examines renewable and fossil fuel efficiency aggregated. Future research endeavours could yield more specific policy insights by conducting analyses at a disaggregated level, considering individual energy sources such as wind, solar, coal and oil. Understanding how the efficiency of each energy source influences carbon efficiency could lead to more targeted and practical policy recommendations.

To the best of the authors’ knowledge, this study addresses a significant gap in the existing literature by being the first empirical investigation into the effects of IQ, fossil fuel efficiency, structural change and RE consumption on carbon efficiency. Unlike prior research, the authors consider a comprehensive IQ index, providing a more holistic perspective. The use of a comprehensive composite index for IQ, coupled with the focus on fossil fuel efficiency and structural change, distinguishes this study from previous research, contributing valuable insights into the intricate dynamics shaping India's path towards enhanced carbon efficiency, an area relatively underexplored in the existing literature.