Search results

Technological innovation, digitalisation and the Industry 4.0 revolution radically changed business management and contributed to the achievement of sustainability goals. While many studies analyse technological innovation, and Industry 4.0 in particular, the technical aspects of its contribution/impact on sustainability remains partially analysed, especially in relation to Industry 4.0 enabling technologies. This study investigates the contribution of Industry 4.0 enabling technologies on sustainability in innovative firms.

The sustainability reports of the 50 most innovative companies based on Boston Consulting Group (BCG)'s 2022 raking is analysed through a content analysis. In the reports, enabling technologies are analysed in relation to their contribution to sustainability.

The results shed light on the application of Industry 4.0 enabling technologies in sustainability practices based on the communication in the firms' sustainability reports. The results indicate that enabling technologies support the three pillars of sustainability in different business processes.

The results have theoretical and managerial implications that broaden the study of enabling technologies and sustainability while also suggesting a future research agenda.

This study aims to address the gap in the literature regarding the contribution of Industry 4.0 enabling technologies to sustainability.

This article sheds light on the missing links concerning the study of using integrated enabling technologies toward sustainable and circular agriculture supply chains by examining the available literature and proposing future research possibilities.

The relevant literature was researched through online databases such as Scopus, Web of Science, Academic Search Premier, Emerald, IEEE Xplore, Science Direct, World Scientific Net and Springer-Link Journals, covering a period from 1999 to 2020. A systematic literature review based on 75 papers analyzed the integration of the concepts of enabling technologies, sustainability, circular economy and supply chain performance in agriculture supply chains.

It was identified that enabling technologies and agriculture supply chains alone have been explored further than integrated enabling technologies, sustainability, circular economy, supply chain performance and agriculture supply chains. Enabling technologies and agriculture supply chains' main findings are: enabling technologies have been studied to improve food safety, food quality and traceability in agriculture supply chains. The main results regarding integrated enabling technologies, sustainability, circular economy, supply chain performance and agriculture supply chains are: Internet of Things and information communication technology play an important role in addressing food security, traceability and food quality, which help achieve sustainable development goals.

This review study provides 13 research questions to underpin future trends regarding integrated technologies' application in agriculture supply chains for circular and sustainable growth.

The purpose of this paper is to investigate, from a thorough review of the literature, the role of metaverse-based quality 4.0 (MV-based Q4.0) in achieving manufacturing resilience (MFGRES). Based on a categorization of MV-based Q4.0 enabler technologies and MFGRES antecedents, the paper provides a conceptual framework depicting the relationship between both areas while exploring existing knowledge in current literature.

The paper is structured as a comprehensive systematic literature review (SLR) at the intersection of MV-based Q4.0 and MFGRES fields. From the Scopus database up to 2023, a final sample of 182 papers is selected based on the inclusion/exclusion criteria that shape the knowledge base of the research.

In light of the classification of reviewed papers, the findings show that artificial intelligence is especially well-suited to enhancing MFGRES. Transparency and flexibility are the resilience enablers that gain most from the implementation of MV-based Q4.0. Through analysis and synthesis of the literature, the study reveals the lack of an integrated approach combining both MV-based Q4.0 and MFGRES. This is particularly clear during disruptions.

This study has a significant impact on managers and businesses. It also advances knowledge of the importance of MV-based Q4.0 in achieving MFGRES and gaining its full rewards.

This paper makes significant recommendations for academics, particularly those who are interested in the metaverse concept within MFGRES. The study also helps managers by illuminating a key area to concentrate on for the improvement of MFGRES within their organizations. In light of this, future research directions are suggested.

This study examined the influence of income and health insurance on the health-seeking behaviour of rural residents, addressing the concerns of endogeneity and heterogeneity bias.

A two-stage residual inclusion was utilised to correct self-selection-based endogeneity problems arising from health insurance membership.

This study provides support for Andersen's behavioural model (ABM). Income and health insurance positively stimulate rural residents' use of modern healthcare services, but the effect of insurance risks a downward bias if treated as exogenous. Further, the effect of health insurance differs between males and females and between adults and the elderly.

This study advances the literature, arguing that, within the ABM framework, enabling (i.e. income and insurance) and predisposing factors (i.e. age and gender) complement each other in explaining rural residents' use of modern health services.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2023-0223

The importance of learning in development in increasingly dynamic contexts can help individuals and organizations adapt to disruption. Artificial intelligence (AI) is emerging as a disruptive technology, with increasing adoption by various human resource management (HRM) functions. However, learning and development (L&D) adoption of AI is lagging, and there is a need to understand of this low adoption based on the internal/external contexts and organization types. Building on open system theory and adopting a technology-in-practice lens, the authors examine the various L&D approaches and the roles of human and technology agencies, enabled by differing structures, different types of organizations and the use of AI in L&D.

Through a qualitative interview design, data were collected from 27 key stakeholders and L&D professionals of MSMEs, NGOs and MNEs organizations. The authors used Gioia's qualitative research approach for the thematic analysis of the collected data.

The authors argue that human and technology agencies develop organizational protocols and structures consistent with their internal/external contexts, resource availability and technology adoptions. While the reasons for lagging AI adoption in L&D were determined, the future potential of AI to support L&D also emerges. The authors theorize about the socialization of human and technology-mediated interactions to develop three emerging structures for L&D in organizations of various sizes, industries, sectors and internal/external contexts.

The study hinges on open system theory (OST) and technology-in-practice to demonstrate the interdependence and inseparability of human activity, technological advancement and capability, and structured contexts. The authors examine the reasons for lagging AI adoption in L&D and how agentic focus shifts contingent on the organization's internal/external contexts.

While AI-HRM scholarship has primarily relied on psychological theories to examine impact and outcomes, the authors adopt the OST and technology in practice lens to explain how organizational contexts, resources and technology adoption may influence L&D. This study investigates the use of AI-based technology and its enabling factors for L&D, which has been under-researched.

Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.

The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.

The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.

This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.

This study aims to assess barriers in accessing and uptaking climate change adaptation information among smallholder tomato farmers in Tanzania.

This study was conducted in Iringa and Morogoro regions, Tanzania. It adopted cross-sectional research design. Multistage, purposive and random sampling techniques with a sample size of 183 smallholder tomato farmers were used. Questionnaires containing closed- and open-ended questions, interview guide and focus group discussions were used to collect data. To ensure validity and reliability of data collection instruments, pretesting of the questionnaires was conducted by administering them to a small group of smallholder tomato growers. Statistical Product and Service Solutions software was used to analyse quantitative data while NVivo was used for analysing qualitative data.

Lack of clear understanding of climate change information (132, 72.1%), scarcity of libraries and information centres (125, 68.3%), inadequate income (125, 68.3%), lack of trust of sources of information (114, 62.3%), lack of sufficient knowledge about climate change (111, 60%), illiteracy (110, 60.1%), poor distribution of electricity in rural areas (109, 59.6%) and use of jargons (93, 50.8%) in explaining climate change adaptation issues are hindrances for smallholder tomato farmers to access and uptake climate change adaptation information.

This study was conducted in only two districts in Tanzania; thus, the findings cannot be generalized.

The government and relevant agencies are recommended to use appropriate strategies for breaking the barriers that impede easy access and uptake of climate change adaptation information by smallholder tomato farmers as one of the interventions to combat the impacts posed by climate change.

This study has contributed to the ongoing discourses on climate change issues. Such discourses have influenced attention and understanding of the role of access and uptake of climate change adaptation information by smallholder farmers for planning and implementation of adaptation strategies through awareness creation.

This study is in line with goal number 13.3 of Sustainable Development Goals, which vows on taking necessary actions in combating climate change and its impacts. Thus, smallholder tomato farmers’ awareness about climate change through access and uptake of climate change adaptation information is one of the interventions to combat climate change and its impacts.

The Internet of things (IoT), an emerging research field, offers solutions to several problems and may result in a paradigm shift in various areas, including education. However, this approach has been under-utilised. Therefore, this research investigates and highlights the primary factors that influence the impact of the IoT on education and reveals the current state of academic research to manage higher education (HE) resources effectively and efficiently.

Data from 35 academic papers were collected and analysed to understand the current situation and assess the readiness of HE to adopt IoT. A literature review is a well-established method for developing knowledge and interpreting issues under consideration. This study systematically analysed the various research methodologies used to adopt IoT, summarising the content of the studies and highlighting the main factors that may affect IoT adoption in HE.

The authors examined 95 papers; 35 were investigated and analysed. The literature review and analysis of academic papers revealed the factors influencing the adoption of IoT technology in HE.

By examining the evidence, this study contributes to understanding the context and supplements existing research. It conducts a systematic literature review to assess the impact of the IoT on the educational process, proposes future research directions and presents findings that aid the efficient management of HE resources.

This paper aims to investigate whether and how enterprise social media (ESM) affordances affect employee agility.

Adopting self-determination theory (SDT), this study examines a model in which the four ESM affordances (i.e. visibility, association, editability and persistence) impact employee agility through the three basic psychological needs satisfaction (i.e. perceived autonomy, perceived relatedness and perceived competence) of employees. Mplus 7.4 was used to analyze survey data gathered from 304 employees who used ESM in the workplace.

The authors’ findings show that all four ESM affordances contribute to perceived relatedness and perceived competence; visibility and association affordances also have positive impacts on perceived autonomy; and all three psychological needs satisfaction positively impact employee agility.

First, this study adapted SDT to explore how ESM influences employee agility. Second, this study enriches the relevant research on the antecedents of employee agility and also provides new evidence and theoretical support for employee agility. Third, this study effectively expands the antecedents and outcomes of employee basic psychological needs satisfaction in the domain of ESM and agility.

The authors originally explore the factors for blockchain technology (BCT) adoption in agricultural supply chains (ASCs) to enhance circularity and understand the dependencies, hierarchical structure and causalities between these factors.

Based on an extant literature review and expert opinion, the present study identified ten enablers for adopting BCT to leverage the circular economy (CE) practices in the ASCs. Then, using an integrated interpretive structural modeling and decision-making trial and evaluation laboratory (ISM-DEMATEL) approach, hierarchical and cause–effect relationships are established.

It was observed that traceability is the most prominent enabler from the CE perspective in ASCs. However, traceability, being a net effect enabler, will be realized through the achievement of other cause enablers, such as seamless connectivity and information flow and decentralized and distributed ledger technology. The authors also propose a 12 Rs framework for enhancing circularity in ASC operations.

The paper identifies enablers to BCT adoption that will enhance circularity in ASC operations. The ISM hierarchical model is based on the driving and dependence powers of the enablers, and DEMATEL aids in identifying causal relationships among the enablers.

The study's findings and proposed 12 Rs framework may help the practitioners and policymakers devise effective BCT implementation strategies in ASCs, thereby empowering sustainability and circularity.

This study enriches the literature by identifying and modeling enablers for BCT adoption in ASCs. The study also proposes a new 12 Rs framework to help enhance ASC circularity.