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This study aims to investigate whether the increasing robot adoption will affect employment rate and wages to contribute to the economic cycle and sustainable development in the world.

The authors introduce a two-way fixed effect model and ordinary least-squares (OLS) model to evaluate the influence based on relevant data of the eighteen countries with the largest robot stocks and robot densities in the world from 2006 to 2019 to test the influences and do the robustness test and endogeneity test by using empirical models.

The authors’ research findings suggest that increasing robot adoption can cause strong negative impacts on employment for both males and females in these economies. Second, the effect of robots on reducing job opportunities has penetrated different industries. It means that this negative impact of robots is comprehensive for the industry. Third, robot adoption can have a strong positive influence on wages and increase workers' incomes.

The limitations of the study are that the influence of industrial intelligence technologies on the circular economy is diversities in different countries. Thus, this study should consider the development levels of different economies to do additional confirmatory studies.

This study makes out the correlations between industrial robots and the employment market from the circular economy perspective. The result proves the existence of this influence relationship, and the authors propose some suggestions to promote sustainable economic development.

This paper addresses the activity of industrial intelligence technologies in the labor market. The employment market is an important part of the circular economy, and it will benefit social development if the government provides appropriate guidance for social investment and industrial layout.

This study is one of the few studies which considered the impact of industrial robots on employment and wages from the perspective of different industries, and this is very important for the circular economy in the world. The results of this paper provide an instructive reference for government policymakers and other countries to stabilize the labor market and optimize human resources for sustainable economic development.

To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous massage path planning and stable interaction control.

First, back region extraction and acupoint recognition based on deep learning is proposed, which provides a basis for determining the working area and path points of the robot. Second, to realize the standard approach and movement trajectory of the expert massage, 3D reconstruction and path planning of the massage area are performed, and normal vectors are calculated to control the normal orientation of robot-end. Finally, to cope with the soft and hard changes of human tissue state and body movement, an adaptive force tracking control strategy is presented to compensate the uncertainty of environmental position and tissue hardness online.

Improved network model can accomplish the acupoint recognition task with a large accuracy and integrate the point cloud to generate massage trajectories adapted to the shape of the human body. Experimental results show that the adaptive force tracking control can obtain a relatively smooth force, and the error is basically within ± 0.2 N during the online experiment.

This paper incorporates deep learning, 3D reconstruction and impedance control, the robot can understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot–human contact.

This study aims to realize efficient, fast and safe robot search task, the belief criteria decision-making approach is proposed to solve the object search task with an uncertain location.

The study formulates the robot search task as a partially observable Markov decision process, uses the semantic information to evaluate the belief state and designs the belief criteria decision-making approach. A cost function considering a trade-off among belief state, path length and movement effort is modelled to select the next best location in path planning.

The semantic information is successfully modelled and propagated, which can represent the belief of finding object. The belief criteria decision-making (BCDM) approach is evaluated in both Gazebo simulation platform and physical experiments. Compared to greedy, uniform and random methods, the performance index of path length and execution time is superior by BCDM approach.

The prior knowledge of robot working environment, especially semantic information, can be used for path planning to achieve efficient task execution in path length and execution time. The modelling and updating of environment information can lead a promising research topic to realize a more intelligent decision-making method for object search task.

The purpose of this paper is to evaluate the barriers militating against the adoption of robotics in the construction industry.

Robotics implementation barriers were obtained from the previous studies and then through questionnaire survey construction stakeholders in Nigeria evaluate these barriers. Consequently, these barriers were examined via the exploratory factor analysis (EFA) technique. Furthermore, a model of these barriers was implemented by means of a partial least square structural equation modeling (PLS-SEM).

The EFA results showed that these barriers could be categorized into two: cost and technology. Results obtained from the proposed model showed that platform tools were crucial tools for implementing cloud computing.

The novelty of this research work will be provided a solid foundation for critically assessing and appreciating the different barriers affecting the adoption of robotics.

Increasing personnel costs and labour shortages have pushed retailers to give increasing attention to their intralogistics operations. We study hybrid order picking systems, in which humans and robots share work time, workspace and objectives and are in permanent contact. This necessitates a collaboration of humans and their mechanical coworkers (cobots).

Through a longitudinal case study on individual-level technology adaption, we accompanied a pilot testing of an industrial truck that automatically follows order pickers in their travel direction. Grounded on empirical field research and a unique large-scale data set comprising N = 2,086,260 storage location visits, where N = 57,239 storage location visits were performed in a hybrid setting and N = 2,029,021 in a manual setting, we applied a multilevel model to estimate the impact of this cobot settings on task performance.

We show that cobot settings can reduce the time required for picking tasks by as much as 33.57%. Furthermore, practical factors such as product weight, pick density and travel distance mitigate this effect, suggesting that cobots are especially beneficial for short-distance orders.

Given that the literature on hybrid order picking systems has primarily applied simulation approaches, the study is among the first to provide empirical evidence from a real-world setting. The results are discussed from the perspective of Industry 5.0 and can prevent managers from making investment decisions into ineffective robotic technology.

The purpose of this study is to evaluate food supply chain stakeholders’ intention to use Industry 5.0 (I5.0) drones for cleaner production in food supply chains.

The authors used a quantitative research design and collected data using an online survey administered to a sample of 264 food supply chain stakeholders in Nigeria. The partial least square structural equation model was conducted to assess the research’s hypothesised relationships.

The authors provide empirical evidence to support the contributions of I5.0 drones for cleaner production. The findings showed that food supply chain stakeholders are more concerned with the use of I5.0 drones in specific operations, such as reducing plant diseases, which invariably enhances cleaner production. However, there is less inclination to drone adoption if the aim was pollution reduction, predicting seasonal output and addressing workers’ health and safety challenges. The findings outline the need for awareness to promote the use of drones for addressing workers’ hazard challenges and knowledge transfer on the potentials of I5.0 in emerging economies.

To the best of the authors’ knowledge, this study is the first to address I5.0 drones’ adoption using a sustainability model. The authors contribute to existing literature by extending the sustainability model to identify the contributions of drone use in promoting cleaner production through addressing specific system operations. This study addresses the gap by augmenting a sustainability model, suggesting that technology adoption for sustainability is motivated by curbing challenges categorised as drivers and mediators.

This study aims to research the usage of virtual reality as a tool to train employees in a changeover process in a company, and if it guarantees the same level of quality. Moreover, another goal is to understand how it can influence the machines’ availability, reducing the number of hours a machine is stopped to perform training sessions.

To achieve the goals proposed, the action research strategy was used. After defining the context and purpose, two full cycles were applied, composed by five activities: diagnosis, action planning, action taking, action evaluation and specify the learning.

The results are extremely satisfactory. On the one hand, it can be concluded that virtual reality is a reliable training tool, guaranteeing proper training to the employees. On the other hand, based on the previous point, the researchers were able to conclude that it is possible to increase machines’ availability, reducing by 50% the time needed to have the machines stopped to run a changeover training.

To the authors’ knowledge, this is the first study to use the action research strategy to develop a virtual reality simulation as a training tool for machines’ changeover in the automotive industry.

Over the next decade, humanity is going to face big environmental problems, and considering these serious issues, businesses are adopting environmentally responsible practices. To put forward specific measures to achieve a more prosperous environmental future, this study aims to develop an environment-based perspective framework by integrating the Internet of Things (IoT) technology into a sustainable automotive supply chain (SASC).

The study presents a conceptual environmental framework – based on 29 factors constituting four stakeholders' rectifications – that holistically assess the SASC operations as part of the ReSOLVE model utilizing IoT. Then, experts from the SASC, IoT and sustainability areas participated in two rigorous rounds of a Delphi study to validate the framework.

The results indicate that the conceptual environmental framework proposed would help companies enhance the connectivity between major IoT tools in SASC, which would help develop congruent strategies for inducing sustainable growth.

This study adds value to existing knowledge on SASC sustainability and digitalization in the context where the SASC is under enormous pressure, competitiveness and increased variability.

Concrete workers perform physically demanding work in awkward postures, exposing their backs to musculoskeletal disorders. Back-support exoskeletons are promising ergonomic interventions designed to reduce the risks of back disorders. However, the suitability of exoskeletons for enhancing performance of concrete workers has not been largely explored. This study aims to assess a passive back-support exoskeleton for concrete work in terms of the impact on the body, usability and benefits of the exoskeleton, and potential design modifications.

Concrete workers performed work with a passive back-support exoskeleton. Subjective and qualitative measures were employed to capture their perception of the exoskeleton, at the middle and end of the work, in terms of discomfort to their body parts, ease of use, comfort, performance and safety of the exoskeleton, and their experience using the exoskeleton. These were analyzed using descriptive statistics and thematic analysis.

The exoskeleton reduced stress on the lower back but caused discomfort to other body parts. Significant correlations were observed between perceived discomfort and usability measures. Design modifications are needed to improve the compatibility of the exoskeleton with the existing safety gears, reduce discomfort at chest and thigh, and improve ease of use of the exoskeleton.

The study was conducted with eight concrete workers who used the exoskeleton for four hours.

This study contributes to existing knowledge on human-wearable robot interaction and provides suggestions for adapting exoskeleton designs for construction work.

Industry 4.0 has put forward a smart perspective on managing supply chain networks and their operations. The current manufacturing system is primarily data-driven. Industries are deploying new emerging technologies in their operations to build a competitive edge in the business environment; however, the true potential of smart manufacturing has not yet been fully unveiled. This research aims to extensively analyse emerging technologies and their interconnection with smart manufacturing in developing smarter supply chains.

This research endeavours to establish a conceptual framework for a smart supply chain. A real case study on a smart factory is conducted to demonstrate the validity of this framework for building smarter supply chains. A comparative analysis is carried out between conventional and smart supply chains to ascertain the advantages of smart supply chains. In addition, a thorough investigation of the several factors needed to transition from smart to smarter supply chains is undertaken.

The integration of smart technology exemplifies the ability to improve the efficiency of supply chain operations. Research findings indicate that transitioning to a smart factory radically enhances productivity, quality assurance, data privacy and labour efficiency. The outcomes of this research will help academic and industrial sectors critically comprehend technological breakthroughs and their applications in smart supply chains.

This study highlights the implications of incorporating smart technologies into supply chain operations, specifically in smart purchasing, smart factory operations, smart warehousing and smart customer performance. A paradigm transition from conventional, smart to smarter supply chains offers a comprehensive perspective on the evolving dynamics in automation, optimisation and manufacturing technology domains, ultimately leading to the emergence of Industry 5.0.