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This paper uses dialectical inquiry to explore tensions that arise when adopting Industry 4.0 technologies in a lean production system and their reconciliation mechanisms.

We conducted an in-depth qualitative case study over a 3-year period on an Italian division of an international electrotechnical organisation that produces electrical switches. This organisation successfully adopted Industry 4.0 technologies in a lean production system. The study is based on primary data such as observations and semi-structured interviews, along with secondary data.

We identify four empirically validated dialectic tensions arising across different Industry 4.0 adoption stages due to managers’ and workers’ contrasting interpretations of technologies. Consequently, we define the related reconciliation mechanisms that allow the effective adoption of various Industry 4.0 technologies to support a lean production system.

This is the first empirical investigation of tensions in the adoption of Industry 4.0 technologies in a lean production system. Furthermore, the paper presents four theoretical propositions and a conceptual model describing which tensions arise during the adoption of Industry 4.0 technologies in a lean production system and the reconciliation mechanisms that prevent lean production system deterioration.

This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.

In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.

The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.

In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

The purpose of this study is to explore how Industry 4.0 (I40) technologies support workers' engagement in soft total quality management (TQM) practices for organisational performance.

The authors conducted a multiple case study of six Italian manufacturing organisations that operate with I40 production and implement TQM practices. The authors concentrated on the relationship between I40 technologies and soft TQM aspects.

I40 technologies provide two forms of engagement with workers. Workers can act as machine supervisors and expert assembly operators. Organisations use five soft TQM practices to involve and develop workers for TQM that vary according to automation levels. The five soft TQM practices are top management design around workers, incremental trials with I40 technologies, worker empowerment, I40 sociotechnical collaboration and individual feedback systems.

In the literature that focusses primarily on how I40 technologies support the hard side of TQM by creating a data-driven and automated quality management system, the authors illustrate how the workforce can be engaged in I40 with five soft TQM practices to improve organisational performance. Thus, the authors complement the theory of hard and soft TQM aspects for I40 production systems.

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.

Industry 4.0 has an inimitable potential to create competitive advantages for the apparel industry by enhancing productivity, production, profitability, efficiency and effectiveness. This study, thus, aims to assess the digitalisation level of the Tanzanian apparel industry through the Industry 4.0 perspectives.

A mixed-methods-based approach was deployed. This study deployed semi-structured interviews, document review and observation methods for the qualitative approach. For the quantitative approach, closed-ended questionnaires were used to ascertain the digitalisation levels and maturity level of the textiles and apparel (T&A) factories and small and medium-sized textile enterprises in Tanzania. The sample size was 110, with participants engaged through the purposive sampling technique.

Industry 4.0 frameworks evolved into practices mainly since 2011 in several service and manufacturing industries globally. For Tanzania, the findings indicate that the overall maturity level of the T&A industries is 2.5 out of 5.0, demonstrating a medium level of adoption. Thus, the apparel industries are not operating under the industry 4.0 framework; they are operating within the third industrial revolution – Industry 3.0 – framework. For such industries to operate within the fourth industrial revolution – Industry 4.0 – that is only possible if there is significantly well-developed industrial infrastructure, availability of engineering talent, stable commercial partnerships, demand from the marketplace and transactional relationship with customers.

This study’s limitations include: firstly, Industry 4.0 is an emerging area; this resulted in limited theoretical underpinnings in the Tanzanian perspectives. Secondly, the studied industries may not suffice the need to generalise the findings for the entire country, thus needing another study.

Although Industry 4.0 conceptual frameworks have been on trial in several industries since 2011, this is amongst the first empirical research on Industry 4.0 in the Tanzanian apparel industry that assesses the digitalisation levels.

Technology-enabled business-to-business (B2B) services contribute the largest share to GDP growth and are fundamental for an economy’s value creation. This article aims to identify key service- and digital technology-driven B2B innovation modes and proposes a research agenda for further exploration.

This conceptual paper adopts a techno-demarcation view on service innovation, encompassing three core dimensions: service offering (the service product, or the “what”), service process (the “how”) and service ecosystem (the “who/for whom”). It delineates the implications of three digital technologies – the internet-of-things (IoT), intelligent automation (IA) and digital platforms – for service innovation across these core dimensions in B2B markets.

Digital technology has immense potential ramifications for value creation by reshaping all three core dimensions of service innovation. Specifically, IoT can transform physical resources into reconfigurable service products, IA can augment and automate a rapidly expanding array of service processes, while digital platforms provide the technical and organizational infrastructure for the integration of resources and stakeholders within service ecosystems.

This study suggests an agenda with six themes for further research, each linked to one or more of the three service innovation dimensions. They are (1) new recurring revenue models, (2) service innovation in the metaverse, (3) scaling up service innovations, (4) ecosystem innovations, (5) power dependency and lock-in effects and (6) security and responsibility in digital domains.

Robotic Process Automation (RPA) is a digital transformation tool that demonstrated tremendous growth in research output as well as its application in the past decade. This study attempts to identify essential research gaps and proposes future research agendas in the field by analyzing publishing trends, major stakeholders (authors, countries, affiliations, journals), key clusters and evolving themes by mapping the most recent research (2016–2022) in the field.

Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) was used to retrieve a total of 244 publications from the Web of Science (WOS) database for this analysis. The study then uses the open-source R program bibliometrix to conduct bibliometric analysis. A variety of tools and methods including collaboration network, word dynamics, co-occurrence network, thematic map and strategy map were utilized.

The analysis reveals the most influential stakeholders (country: the USA, author: Arai K, affiliation: Christ Deemed University), main clusters of intellectual structure (process mining, digital transformation, blockchain, information systems) and the evolution of themes (model innovation, artificial intelligence, big-data, design science and user acceptance) in the subject.

This study uses bibliometric analysis to provide a comprehensive overview of RPA literature which unravels the conceptual structure of the stream and proposes a research agenda for the future. Based on the growth of themes and the strategy map, this study may assist entrepreneurs and practitioners in determining field priorities for strategizing process innovation.

This study aims to chart the impact of technological advancements on tourism from the post–Second World War era to the present and forecast their influence until 2050. It assesses how technologies have reshaped travel experiences and operations, with a focus on upcoming innovations such as the metaverse, Web 3.0 and AI, and their implications for sustainable and ethical tourism.

This study uses a hybrid approach, combining historical analysis and future projections. It analyzes archival data, industry reports and academic literature.

This study identifies crucial technological milestones that have significantly impacted tourism, including the rise of commercial aviation, the internet and AI. Future trends suggest emerging technologies will further transform the sector. Challenges in sustainability, ethics and inclusivity are highlighted as critical considerations for future development.

This paper offers a unique longitudinal perspective on technology’s influence on tourism, bridging past trends with future projections.

本研究采取混合方法, 融合历史分析与未来趋势预测。研究分析了丰富的档案数据、行业报告以及学术文献。

旨在勾勒从二战后至今技术进步对旅游业的影响, 并展望至2050年的潜在影响。本研究着重评估技术如何重塑旅游体验和运作, 特别是对元宇宙、网络3.0和人工智能等即将到来的创新技术及其对可持续和伦理旅游的意义。

识别了旅游业中关键的技术里程碑, 包括商业航空、互联网和人工智能的崛起。研究指出, 未来趋势显示新兴技术将继续深刻改变旅游业。同时强调, 可持续性、伦理和包容性是未来发展中不可忽视的关键要素。

本文从独特的纵向视角出发, 深入探讨了技术对旅游业的历史与未来影响, 将过去发展趋势与未来展望紧密结合。

Este estudio emplea un enfoque híbrido que combina el análisis histórico y las proyecciones de futuro. Analiza datos de archivo, informes del sector y bibliografía académica.

La investigación pretende trazar el impacto de los avances tecnológicos en el turismo desde la era posterior a la Segunda Guerra Mundial hasta la actualidad y prever su influencia hasta 2050. Evalúa cómo las tecnologías han reconfigurado las experiencias y las operaciones de viaje, centrándose en las próximas innovaciones como el Metaverso, la Web 3.0 y la IA, y sus implicaciones para un turismo sostenible y ético.

El estudio identifica hitos tecnológicos cruciales que han tenido un impacto significativo en el turismo, como el auge de la aviación comercial, Internet y la IA. Las tendencias futuras sugieren que las tecnologías emergentes transformarán aún más el sector. Los retos en sostenibilidad, ética e inclusividad se destacan como consideraciones críticas para el desarrollo futuro.

Este artículo ofrece una perspectiva longitudinal única sobre la influencia de la tecnología en el turismo, tendiendo un puente entre las tendencias pasadas y las proyecciones futuras.

The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.

The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.

The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.

Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.

First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.

As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.

This study aims to improve the use of digitalization in facilities management (FM) for shopping complex facilities in the post-COVID-19 era. The resumption of economic activities, especially in shopping complexes, poses challenges for FM with throngs of shoppers. To tackle these challenges, enhanced and innovative FM practices are necessary.

The study used a qualitative research approach, incorporating case studies, interviews, observations and documentation. It focused on super-regional shopping complexes in the Klang Valley, Malaysia, selecting two complexes for qualitative data collection. Supplementary data were gathered from various sources, including government policy publications, websites, books, journal papers and archival records.

The research provides valuable insights into FM innovations and the application of FM digitalization in shopping complexes after the COVID-19 pandemic. It also addresses challenges faced by FM teams during this period. Recommendations for implementing FM digitalization in super-regional shopping complexes post-COVID-19 include developing skilled personnel, defining appropriate work scopes, strategies and policies, using cost-effective software, and increasing occupant awareness. The involvement of outsourced service providers is advised, emphasizing their understanding of the organization’s business model and innovative approaches.

The findings offer new perspectives on the characteristics of FM digitalization in the commercial sector during business disruptions caused by the pandemic. The proposed strategies are grounded in real industry implementations, aiming to enhance the FM digitalization approach for improved business performance.