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Rebar is the prime component of reinforced concrete structures, and rebar monitoring is a time-consuming and technical job. With the emergence of the fourth industrial revolution, the construction industry practices have evolved toward digitalization. Still, hesitation remains among stakeholders toward the adoption of advanced technologies and one of the significant reasons is the unavailability of knowledge frameworks and implementation guidelines. This study aims to investigate technical factors impacting automated monitoring of rebar for the understanding, confidence gain and effective implementation by construction industry stakeholders.

A structured study pipeline has been adopted, which includes a systematic literature collection, semistructured interviews, pilot survey, questionnaire survey and statistical analyses via merging two techniques, i.e. structural equation modeling and relative importance index.

The achieved model highlights “digital images” and “scanning” as two main categories being adopted for automated rebar monitoring. Moreover, “external influence”, “data-capturing”, “image quality”, and “environment” have been identified as the main factors under “digital images”. On the other hand, “object distance”, “rebar shape”, “occlusion” and “rebar spacing” have been highlighted as the main contributing factors under “scanning”.

The study provides a base guideline for the construction industry stakeholders to gain confidence in automated monitoring of rebar via vision-based technologies and effective implementation of the progress-monitoring processes. This study, via structured data collection, performed qualitative and quantitative analyses to investigate technical factors for effective rebar monitoring via vision-based technologies in the form of a mathematical model.

This study empirically investigates the impacts of technological innovation and operational efficiency on environmental performance and the moderating effects of environmental orientation.

We develop a conceptual framework based on the Porter Hypothesis. We collect a sample of 850 listed firms in China between 2010 and 2019. The fixed effect model was used to analyse the data.

The empirical findings reveal that technological innovation indirectly enhances environmental performance through operational efficiency and partially mediates this impact. We also find that environmental orientation strengthens the positive impacts of technological innovation and operational efficiency on environmental performance.

This study contributes to the literature by revealing that technological innovation is positively associated with operational efficiency and environmental performance, which suggests that technological innovation can simultaneously enhance business and environmental performance. Hence, this study provides empirical support for the Porter Hypothesis. The results also extend the Porter Hypothesis by revealing how technological innovation affects environmental performance and under what conditions technological innovation has a greater impact on environmental performance.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

The objective of this study is to examine the various factors that exert an influence on the green economy. This study also investigates the impact of foreign direct investment (FDI) on the Malaysian economy, specifically focusing on its position as a mediator. This research also examines the correlation between FDI and its influence on the contemporary green economy.

The authors employed quantitative methodologies and a self-administered survey to evaluate data and derive a definitive conclusion. The result was constructed using SPSS and SEM-PLS as the analytical software.

The study reveals that technological advancement, investment country and government policy significantly and positively affect the green economy, catalyse SDG goals and restructure the economy in better shape.

The current empirical research bridges the research gap in the context of technology advancement in government policy from emerging economies by exploring important factors, proposing their impact on the performance of the green economy, and empirically testing those hypothesized relationships. This study deciphers that FDI influences the green economy, where the investment country plays a significant role. Also, for a graphical presentation of this abstract, see the online appendix.

The current chapter proposes a theoretical framework to assess the sustainability of economic growth in the Gulf Cooperation Council (GCC) States. The authors integrate insights from endogenous growth models and consider the unique socioeconomic characteristics of the GCC region to provide a comprehensive and tailored approach to understanding the determinants of economic growth and formulating effective policy measures to foster sustainable development and growth. This chapter highlights the environmental challenges faced by GCC; based on this, the authors suggested indicators to construct a theoretical framework (Economic Growth, Climatic Indicators, Energy Indicators, Social Indicators, and Economic Resources Indicators). The authors propose that policymakers and researchers in GCC States should take these factors into account when devising policies or conducting research aimed at fostering sustainable economic growth. Overall, this chapter presents significant insights for policymakers, researchers, and stakeholders involved in promoting the sustainable economic advancement of the GCC States.

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.

Due to the rapid development of artificial intelligence (AI) technology, increasing the use of AI in healthcare is critical, but few studies have explored the extent to which physicians cooperate with AI in their work to achieve productive and innovative performance, which is a key issue in operations management (OM). We conducted empirical research to answer this question.

We developed a conceptual model based on the ambidextrous perspective. To test our model, we collected data from 200 Chinese hospitals. One senior and one junior physician from each hospital participated in this research so that we could get a more comprehensive view. Based on the sample of 400 participants and the conceptual model, we examined whether different types of AI use have distinct impacts on physicians’ productivity and innovation by conducting hierarchical regression and post hoc tests. We also introduced team psychological safety climate (TPSC) and AI technology uncertainty (AITU) as moderators to investigate this topic in further detail.

We found that augmentation AI use is positively related to overall productivity and innovative job performance, while automation AI use is negatively related to these two outcomes. Furthermore, we focused on the impacts of the ambidextrous use of AI on these two outcomes. The results highlight the positive impacts of complementary use on both outcomes and the negative impact of balance on innovative job performance. TPSC enhances the positive impacts of complementary use on productivity, whereas AITU inhibits the negative impacts of automation and balanced use on innovative job performance.

In the age of AI, organizations face greater trade-offs between performance and technology management. This study contributes to the OM literature from the perspectives of operational performance and technology management in three ways. First, it distinguishes among different AI implementations and their diverse impacts on productivity and innovative performance. Second, it identifies the different conditions under which automation AI use and augmentation are superior. Third, it extends the ambidextrous perspective by becoming an early adopter of this approach to explore the implications of different types of AI use in light of contingency factors.

Emerging economies have been highlighted as an important growth source of the global economy. However, this group of countries has not received enough academic attention yet. Therefore, this study aims to identify the impact of research and development (R&D) expenditure on economic growth in emerging economies.

The theoretical framework of the production function is applied to quantitatively analyse the impact of R&D expenditure on economic growth with a sample of 29 emerging economies in the period between 1996 and 2019.

The panel cointegration test confirms the existence of long-run cointegration relationships between economic growth and independent variables in these emerging economies. Besides, the estimated results show that the national R&D expenditure has positive effects on economic growth from both direct and interaction dimensions. This evidence has filled the empirical research gap in the R&D-growth nexus in the case of emerging economies. Finally, while gross capital and education have positive impacts on growth, corruption has a harmful effect on economic growth in these countries.

The results highlight that policymakers should enhance R&D expenditure and R&D activities as the key national development strategy. The investment in R&D not only helps emerging economies avoid the middle-income trap but also pushes these countries to successfully join the group of developed countries.

To the best of the authors’ knowledge, this research is among the first to examine the impact of R&D expenditure on economic growth with a homogeneous sample of emerging economies. The results are obviously helpful for policymakers to use R&D as the key development strategy for supporting economic growth in emerging economies in the future.