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The development of telecare services across the UK has been supported by grants from the respective governments of Scotland and Wales, and by the DH in England. New services are being established to sometimes operate alongside existing community equipment services and community alarm services. Elsewhere they are embracing a wider range of services including rehabilitation, intermediate care and health services designed to reduce the use of unscheduled care services. This paper discusses the difficulties in understanding the scope of telecare services, and the definitions of services that will need to be confirmed so that service users can choose appropriately if offered direct payments. Two different service models are offered, one of which uses telehealth as an umbrella term to cover all telecare, e‐care and m‐care, and telemedicine where the former includes all such services offered in the service user's home, including those of a medical nature. The second model views telecare alongside assistive technologies and telemedicine as one of three different technology groups designed to make people more independent or to bring care closer to home. There is significant overlap between the three groups, which justifies the introduction of a new term ‐ ARTS (assistive and remote technology services) ‐ to describe this area of support.

The purpose of this paper is to propose a framework for understanding, predicting and analyzing how future service technologies can lead to value co-creation at different stages of a value chain.

For organizations, future service technologies are growing in importance and will become a crucial means to survival. It is clear that future service technologies will increase the opportunity to reduce costs and create efficiency, but it is not equally clear how future service technologies enable value creation for customers and users. On this premise, the study proposes a conceptual framework.

The framework illustrates how future service technologies can lead to value creation for customers. The paper also portrays opportunities and potential pitfalls with future service technologies for organizations.

Several researchers are focusing on innovative technologies. Many business companies are talking about how to implement them and increase their profit. However, less attention is devoted to the ways in which future service technologies will lead to benefits and the experience of service for customers and users using them. This paper represents an original attempt to illustrate that.

The purpose of this paper is to describe the first National Technology Roadmap (NTRM) of China, compiled by the Ministry of Science and Technology in 2007, in accordance with the analytical framework of “national needs‐strategic tasks‐key technologies‐development priorities,” and to comment upon its progress.

The technology foresight in 2003‐2006 was organized by the Chinese Ministry of Science and Technology. Based on two large rounds of Delphi investigations involving 1,400 experts and a SWOT analysis of every candidate technology, the key technologies were selected and the characteristics of them acquired. A large number of panels were organized to discuss and affirm the national needs and strategic tasks, so the logic relationships among the key items of NTRM could be clarified.

Five national needs, 30 strategic tasks, and 90 key technologies were promoted, and their relationships constructed. The characteristics of all the 90 technologies, including their R&D basis, gap between their level and the levels of advanced countries, and the possible time needed to realize these technologies were evaluated. The NTRM of China was successfully formulated.

Only nine technology fields were considered in the Delphi investigations, because of the limited study conditions. This might become the limitation to the application of this NTRM. In further study, more technology fields should be involved and all the processes finished as soon as possible.

The results of the NTRM were partly adopted by the 11th science and technology plan of China. Through the progress of NTRM, agreements concerning the key technologies and their development roadmaps were made. Both the NTRM and its compiling process provide important reference points for scientific and technological managers, scientists, and entrepreneurs, because they will help to make better decisions and contribute greatly to the research and development of the technologies.

This is the first attempt in China to make a NTRM. Results from the large‐scale Delphi investigations were taken as the basis for the roadmapping. The model of “national needs‐strategic tasks‐key technologies‐development priorities” was promoted and the technology roadmapping at a national level was conducted successfully.

The purpose of this paper is to present a practical data-based framework for the prioritization of investment in manufacturing technologies, methods and tools, and to demonstrate its applicability and practical relevance through two case studies of manufacturing firms of different industrial segments.

The proposed framework is based on network theory applied on technology adoption. For this, the database of Industry 4.0 maturity assessments of SENAI was used to develop data visualization tools named “Technology Networks”. Thus, this study is descriptive research with correlational design. Besides, the framework was applied in two companies and semi-structured interviews were carried out with domain experts.

The technology networks highlight the technological adoption patterns of six industrial segments, by considering the answers of 863 Brazilian companies. In general, less sophisticated technologies were positioned in the center of the networks, which facilitates the visualization of adoption paths. Moreover, the networks presented a well-balanced adoption scenario of Industry 4.0 related technologies and lean manufacturing methods and tools.

Since the database was not built under an experimental design, it is not expected to make statistical inferences about the variables. Furthermore, the decision to use an available database prevented the editing or inclusion of technologies. Besides, it is estimated that the technology networks given have few years for obsolescence due to the fast pace of technological development.

The framework is a tool that may be used by practicing manufacturing managers and entrepreneurs for taking assertive decisions regarding the adoption of manufacturing technologies, methods and tools. The proposition of using network theory to support decision making on this topic may lead to further studies, developments and adaptations of the framework.

This paper addresses the topics of lean manufacturing and Industry 4.0 in an unprecedented way, by quantifying the adoption of its technologies, methods and tools and presenting it in network visualizations. The main value of this paper is the comprehensive framework that applies the technology networks for supporting decision making regarding technology adoption.

This research study aims to inquire into the technostress phenomenon at an organizational level from machine learning (ML) and artificial intelligence (AI) deployment. The authors investigated the role of ML and AI automation-augmentation paradox and the socio-technical systems as coping mechanisms for technostress management amongst managers.

The authors applied an exploratory qualitative method and conducted in-depth interviews based on a semi-structured interview questionnaire. Data were collected from 26 subject matter experts. The data transcripts were analyzed using thematic content analysis.

The study results indicated that role ambiguity, job insecurity and the technology environment contributed to technostress because of ML and AI technologies deployment. Complexity, uncertainty, reliability and usefulness were primary technology environment-related stress. The novel integration of ML and AI automation-augmentation interdependence, along with socio-technical systems, could be effectively used for technostress management at the organizational level.

This research study contributed to theoretical discourse regarding the technostress in organizations because of increased ML and AI technologies deployment. This study identified the main techno stressors and contributed critical and novel insights regarding the theorization of coping mechanisms for technostress management in organizations from ML and AI deployment.

The phenomenon of technostress because of ML and AI technologies could have restricting effects on organizational performance. Executives could follow the simultaneous deployment of ML and AI technologies-based automation-augmentation strategy along with socio-technical measures to cope with technostress. Managers could support the technical up-skilling of employees, the realization of ML and AI value, the implementation of technology-driven change management and strategic planning of ML and AI technologies deployment.

This research study was among the first few studies providing critical insights regarding the technostress at the organizational level because of ML and AI deployment. This research study integrated the novel theoretical paradigm of ML and AI automation-augmentation paradox and the socio-technical systems as coping mechanisms for technostress management.

Many advanced technologies applied to maintenance are aimed at data analysis and not directly at the execution of activities. Considering the lack of studies that analyze the use of technologies with a focus on aiding maintenance activities, this study aims to investigate the applicability of advanced technologies capable of mitigating ergonomic risks in mining maintenance activities.

A mixed-method study approach was performed in the most important Brazilian mining company, where three groups of equipment were observed: pumps, crushers and sieves. Qualitative and quantitative data were collected, including structured interviews with 60 maintenance professionals for the equipment, and a workshop was held to evaluate the applicability of these technologies in the maintenance activity of this equipment.

It was verified that the load handler, weight cancelers and automatically guided vehicle technologies were assessed as capable of mitigating ergonomic problems of the supporting the weight of parts and tools and the human traction during maintenance activities.

The study observed only one company, and the five technologies analyzed here are not yet a reality in this sector.

This research directs maintenance managers in the implementation of process improvements, in the incorporation of technologies capable of mitigating the ergonomic problems experienced by the maintenance professionals. In this way, it is expected to reduce the number of absences from work and improve the working conditions of these professionals.

Mining activities impact the local economy and are important in the development of technologies that improve productivity and the man–work relationship. The demands of industries for new solutions encourage local technological development through an approximation with university research and development centers. At the same time, it is observed that these centers can help in the formation of competences to act, either in the implementation of these technologies or in their handling. This university–company integration, in addition to benefiting the mining segment, has the potential to expand the solution to different supply chains, which proves to be a relevant social impact.

This study is pioneering in understanding the use of advanced technologies in maintenance activities in the context of the mining industry (extractive primary sector).

This study provides an up-to-date review of additive manufacturing (AM) technologies and guidance for selecting the most appropriate ones for specific applications, taking into account the main features, strengths, and limitations of the existing options.

A literature review on AM technologies was conducted to assess the current state-of-the-art. This was followed by a closer examination of different AM machines to gain a deeper insight into their main features and operational characteristics. The conclusions and data gathered were used to formulate a classification and decision-support framework.

The findings indicate the building blocks of the selection process for AM technologies. Furthermore, this work shows the suitability of the existing AM technologies for specific cases and points to opportunities for technological and decision-support improvements. Lastly, more standardization in AM would be beneficial for future research.

The proposed framework offers valuable support for decision-makers to select the most suitable AM technologies, as demonstrated through practical examples of its utilization. In addition, it can help researchers identify the limitations of AM by pinpointing applications where existing technologies fail to meet the requirements.

The study offers a novel classification and decision-support framework for selecting AM technologies, incorporating machine characteristics, process features, physical properties of printed parts, and costs as key features to evaluate the potential of AM. Additionally, it provides a deeper understanding of these features as well as the potential opportunities for AM and its impact on various industries.

This study aims to investigate the motivations for the adoption of Industry 4.0 technologies among manufacturing firms in developing economies. Specifically, the effects of relative advantage of the technologies, competitive pressure, and government support on the adoption are explored. Moreover, the mediating role of top management support between environmental factors (government support and competitive pressure) and the adoption of Industry 4.0 technologies is examined.

A research model is developed based on the technology-organization-environment (TOE) framework strengthened by institutional theory. Structural equation modeling (SEM) approach is employed to evaluate the model using data obtained from 215 manufacturing firms through a cross-industry survey. Additionally, a post-hoc analysis is conducted using cluster analysis and ANOVA.

The results show that competitive pressure and government support significantly promote top management support, which in turn contributes to the adoption of Industry 4.0 technologies. Relative advantage of the technologies is not significantly related to the adoption.

This study does not explore the relationship between technology type and the specific needs of manufacturing firms. Future researchers can conduct a more comprehensive analysis by examining how different technology types align with the unique needs of individual companies.

The findings of this study have implications for both policymakers and managers. Policymakers can leverage these insights to understand the underlying motivations behind manufacturing firms' adoption of Industry 4.0 technologies and develop promoting policies. In turn, managers should keep an eye on government policies and utilize government support to facilitate technology adoption.

This study uncovers the underlying motivations—government support and competitive pressure—for the adoption of Industry 4.0 technologies among manufacturing firms in developing economies. Meanwhile, it complements previous research by showing the mediating role of top management support between environmental factors (government support and competitive pressure) and the adoption of Industry 4.0 technologies.

This study aims to investigate the influence of smart technologies on SMEs sustainability and to measure the mediation effect of SMEs’ sustainability strategy in the relationship between smart technologies and SMEs’ sustainable performance in the Kingdom of Bahrain. The sustainability concept for the purpose of this study includes environmental sustainability, social sustainability and profitability factors.

The study applied the quantitative analysis method. The sample size was 403 small- and medium-sized enterprises (SMEs) from Bahrain.

The study concludes that smart technology has a major effect on profitability performance, among other sustainable performance factors. In addition, there is no mediation effect of “SMEs’ sustainability strategy”. The study has recommended improving SMEs’ participation in sustainable development principles by considering supportive global initiatives to “Net Zero Roadmap 2050”, increasing the demand for using technologies and including academic “sustainability” concepts in academic programs.

This study contributes significantly to Bahrain’s economic growth by studying the proactive and innovative methods for increasing SMEs’ efficiency. Furthermore, it adds value to Bahrain’s national economy by investigating the role of SMEs and its strategic practices by implementing smart businesses towards developing business empowerment in Bahrain’s economic vision for 2030 and meeting SDGs regionally and globally.

This paper aims at identifying the main contributions of Industry 4.0 (I4.0) technologies to the enhancement of knowledge management (KM).

A scoping review based on two stages was carried out. In the first stage, a numerical and descriptive analysis was conducted. In the second stage, the latent content of those papers was explored through a qualitative analysis, in which papers were assessed regarding the outcome of the implementation of ten I4.0 technologies on 14 KM abilities and 6 KM processes.

Six technologies (e.g. Internet of Things (IoT), big data, cloud computing, simulation, visualisation and industrial robot) were claimed to have a significant contribution on KM, especially for creation, organisation, dissemination and application processes. Overall, results indicated that technologies can significantly contribute to KM, although the extent of such contribution varies across technologies, learning levels, KM processes and abilities.

Existing studies in I4.0 fall short in exploring how embodied knowledge is articulated in practice, especially regarding opportunities to use new technological alternatives in favour of knowledge and learning development. This study complements the existing literature on I4.0 by identifying which technologies can contribute to KM. Also, a framework was proposed to examine the contribution of I4.0 technologies to KM at individual, team, and organisational levels.