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This paper has two objectives: first, to investigate the state-of-the-art of Industry 4.0 (I4.0) adoption in Italian manufacturing firms and, second, to understand variations in technologies implemented and business functions involved, benefits perceived, and obstacles encountered in I4.0 implementation over a three-year period.

The approach adopted in this research is descriptive, nesting longitudinal features. The paper presents a descriptive survey of 102 Italian manufacturing companies. The authors also evaluated non-response biases. The longitudinal approach was achieved by comparing the responses of the 40 sub-samples in common with a second similar survey launched three years prior, which aimed to identify patterns of evolution in the adoption of the I4.0 paradigm.

Survey findings demonstrate that Italian manufacturing companies still have limited awareness of I4.0 technologies, and the adoption of I4.0 technologies differs per technology. Company size and information system coverage level are the two factors that impact the company's technology adoption level. The comparative study shows that knowledge and adoption increase in a three-year interval with an unbalanced involvement of business functions regarding the I4.0 transformation. Indeed, companies are still seeking I4.0 solutions to reduce costs and lead times primarily, and the benefits perceived by companies are shown to be related to the number of I4.0 technologies in use. Finally, when companies put the I4.0 technologies into practice, competence is constantly considered the most significant barrier.

This paper aims at conducting a thorough investigation into the development of I4.0 adoption in manufacturing companies. The main limitation of this study concerns the limited number of subjects involved in the longitudinal study (40) and the focus on a limited geographical area (Italy). In addition, more I4.0 technologies could also be incorporated into the survey protocol to gain further insight into I4.0 development.

The authors provide one of the first attempts to assess the variations of I4.0 implementation concerning technology adoption, business function involvement, and the alteration of benefits and obstacles. Several studies presented in the literature highlight the lack of longitudinal studies investigating the development of the I4.0 paradigm in a specific manufacturing context: this paper is the attempt at filling this gap.

As focal firms in supply networks reflect on their experiences of the pandemic and begin to rethink their operations and supply chains, there is a significant opportunity to leverage digital technological advances to enhance socially responsible operations performance (SROP). This paper develops a novel framework for exploring the adoption of Industry 4.0 technologies for improving SROP. It highlights current best-practice examples and presents future research pathways.

This viewpoint paper argues how Industry 4.0 technology adoption can enable effective SROP in the post-COVID-19 era. Academic articles, relevant grey literature, and insights from industry experts are used to support the development of the framework.

Seven technologies are identified that bring transformational capabilities to SROP, i.e. big data analytics, digital twins, augmented reality, blockchain, 3D printing, artificial intelligence, and the Internet of Things. It is demonstrated how these technologies can help to improve three sub-themes of organisational social performance (employment practices, health and safety, and business practices) and three sub-themes of community social performance (quality of life and social welfare, social governance, and economic welfare and growth).

A research agenda is outlined at the intersection of Industry 4.0 and SROP through the six sub-themes of organisational and community social performance. Further, these are connected through three overarching research agendas: “Trust through Technology”, “Responsible Relationships” and “Freedom through Flexibility”.

Organisational agendas for Industry 4.0 and social responsibility can be complementary. The framework provides insights into how Industry 4.0 technologies can help firms achieve long-term post-COVID-19 recovery, with an emphasis on SROP. This can offer firms competitive advantage in the “new normal” by helping them build back better.

People and communities should be at the heart of decisions about rethinking operations and supply chains. This paper expresses a view on what it entails for organisations to be responsible for the supply chain-wide social wellbeing of employees and the wider community they operate in, and how they can use technology to embed social responsibility in their operations and supply chains.

Contributes to the limited understanding of how Industry 4.0 technologies can lead to socially responsible transformations. A novel framework integrating SROP and Industry 4.0 is presented.

The internationalization of business has grown the production value chains and created performance challenges for industrial production. Industry 4.0, the digital transformation of industrial processes, promises to deliver performance improvements through smart functionalities. This study investigates how digital transformation translates to performance gain by adopting a systems perspective to drive smartness.

This study uses qualitative research to collect data on the lived experiences of digital transformation practitioners for theory development. It uses semi-structured interviews with industry experts and applies the Gioia methodology for analysis.

The study determined that enterprise smartness is an organizational capability developed by digital transformation, it is a function of integration and the enabler of organizational performance gains in the Industry 4.0 context. The study determined that performance gains are experienced in productivity, sustainability, safety and customer experience, which represents performance metrics for Industry 4.0.

This study contributes a model that inserts smartness in the linkage between digital transformation and organizational outcomes to the digital transformation and production management literature.

The study indicates that digital transformation programs should focus on developing smartness rather than technology implementations, which must be considered an enabling activity.

Existing studies recognized the positive impact of technology on performance in industrial production. The study addresses a missing link in the Industry 4.0 value creation process. It adopts a systems perspective to establish the role of smartness in translating technology use to performance outcomes. Smart capabilities have been the critical missing link in the literature on harnessing digital transformation in organizations. The study advances theory development by contributing an Industry 4.0 value model that establishes a link between digital technologies, smartness and organizational performance.

Many manufacturers are exploring adopting smart technologies in their operations, also referred to as the shift towards “Industry 4.0”. Employees' contribution to high-tech initiatives is key to successful Industry 4.0 technology adoption, but few studies have examined the determinants of employee acceptance. This study, therefore, aims to explore how managers affect employees' acceptance of Industry 4.0 technology, and, in turn, Industry 4.0 technology adoption.

Rooted in the unified theory of acceptance and use of technology model and social exchange theory, this inductive research follows an in-depth comparative case study approach. The two studied Dutch manufacturing firms engaged in the adoption of Industry 4.0 technologies in their primary processes, including cyber-physical systems and augmented reality. A mix of qualitative methods was used, consisting of field visits and 14 semi-structured interviews with managers and frontline employees engaged in Industry 4.0 technology adoption.

The cross-case comparison introduces the manager's need to adopt a transformational leadership style for employees to accept Industry 4.0 technology adoption as an organisational-level factor that extends existing Industry 4.0 technology user acceptance theorising. Secondly, manager's and employee's recognition and serving of their own and others' emotions through emotional intelligence are proposed as an additional individual-level factor impacting employees' acceptance and use of Industry 4.0 technologies.

Synthesising these insights with those from the domain of Organisational Behaviour, propositions were derived from theorising the social aspects of effective Industry 4.0 technology adoption.

Industry 4.0 technologies are promising to increase manufacturing companies' performance through the new knowledge that such digital technologies allow to create and manage within the firm boundaries and through customer interactions. Despite the great attention on the Industry 4.0 adoption paths, little is known about the relationships with previous waves of digital technologies, namely, information and communication technologies (ICTs), and how different groups of both types of technologies link to knowledge and its related performances.

The study employed a quantitative research design using a survey method. Submitting the questionnaire to entrepreneurs, chief operation officers or managers in charge of the operational and technological processes of Italian manufacturing firms, 206 respondents stated that their firm has adopted at least one of the seven Industry 4.0 technologies investigated.

The findings of the study highlight the positive relationship between ICT and Industry 4.0 technologies in terms of both intensity and groups of technologies (Web-based, Management and Manufacturing ICT; Operation, Customization and Data-processing 4.0), and how technologies affect knowledge-related performances in terms of products and processes, job-learning, product-related services and customer involvement.

This study is one of the first attempts to link groups of ICT to groups of Industry 4.0 technologies and to explore the effects in terms of knowledge-related performances as a measure of technology use. The study shows strong path dependency among ICT, Industry 4.0 and knowledge performance, enriching the literature on technological innovation and knowledge management.

This paper investigates how manufacturers can develop a learning-to-learn capability for enabling Industry 4.0 adoption.

This research design is guided by our research question: How can manufacturers develop a learning-to-learn capability that enables Industry 4.0 adoption? The authors adopt action research to generate actionable knowledge from a two-year-long action learning intervention at the Danish rooftop window manufacturer VELUX.

Drawing on emergent insights from the action learning intervention, it was found that a learning-to-learn capability based on lean was a core construct and enabler for manufacturers to adopt Industry 4.0 successfully. Institutionalizing an organizational learning scaffold encompassing the intertwined learning processes of systems Alpha, Beta and Gamma served as a significant way to develop a learning-to-learn capability for Industry 4.0 adoption (systematic problem-solving abilities, leaders as learning facilitators, presence of a supportive learning environment and Industry 4.0 knowledge). Moreover, group coaching is a practical action learning intervention for invoking system Gamma and developing leaders to become learning facilitators – an essential leadership role during Industry 4.0 adoption.

The study contributes to theory and practice by adopting action research and action learning to explore learning-to-learn as a core construct for enabling Industry 4.0 adoption and providing a set of conditions for developing a learning-to-learn capability. Furthermore, the study reveals that leaders are required to act as learning facilitators instead of relying on learning about and implementing Industry 4.0 best practices for enabling adoption.

Industry 4.0 defines the application of digital technologies on business infrastructure and processes. With the increasing need to take into account the social and environmental impact of technologies, the concept of Society 5.0 has been proposed to restore the centrality of humans in the proper utilization of technology for the exploitation of innovation opportunities. Despite the identification of humans, resilience and sustainability as the key dimensions of Society 5.0, the definition of the key factors that can enable Innovation in the light of 5.0 principles has not been yet assessed.

An SLR, followed by a content analysis of results and a clustering of the main topics, is performed to (1) identify the key domains and dimensions of the Industry 5.0 paradigm; (2) understand their impact on Innovation 5.0; (3) discuss and reflect on the resulting implications for research, managerial practices and the policy-making process.

The findings allow the elaboration of a multileveled framework to redefine Innovation through the 5.0 paradigm by advancing the need to integrate ICT and technology (Industry 5.0) with the human-centric, social and knowledge-based dimensions (Society 5.0).

The study detects guidelines for managers, entrepreneurs and policy-makers in the adoption of effective strategies to promote human resources and knowledge management for the attainment of multiple innovation outcomes (from technological to data-driven and societal innovation).

The purpose of this paper is to identify actions and guidelines for enabling and fostering the Industry 4.0 adoption, as well as to understand the role of three ecosystem actors in these actions (i.e. companies, educational organizations and regional policy makers).

52 semi-structured expert interviews in the Tyrol-Veneto cross-border macro-region were carried out and interpreted using the innovation ecosystem concept. In particular, drawing from this latter, six ecosystem building blocks were identified and used to analyze the interviews' content.

The findings allow not only to build a comprehensive framework for action to support Industry 4.0 adoption, but also to confirm the importance of exploring Industry 4.0 through the lens of the ecosystem concept. Indeed, the authors show that R&D activities should be complemented with interorganizational actions, such as training and networking, and that all ecosystem actors should be involved in the Industry 4.0 adoption.

This is among the few studies that adopt the innovation ecosystem perspective to explore best practices for Industry 4.0 adoption, thus overcoming the weakness of existing papers based on a firm-level perspective. It also complements previous ecosystem-based research on Industry 4.0 by exploring the technology adoption side, rather than the technology provision one, and by considering the adoption of a wide set of technologies.

This study aims to provide a measurement model, and the underlying constructs and items, for Logistics 4.0 in manufacturing companies. Industry 4.0 technology for logistics processes has been termed Logistics 4.0. Logistics 4.0 and its elements have seen varied conceptualizations in the literature. The literature has mainly focused on conceptual and theoretical studies, which supports the notion that Logistics 4.0 is a relatively young area of research. Refinement of constructs and building consensus perspectives and definitions is necessary for practical and theoretical advances in this area.

Based on a detailed literature review and practitioner focus group interviews, items of Logistics 4.0 for manufacturing enterprises were further validated by using a large-scale survey with practicing experts from organizations located in Central Europe, the Northeastern United States of America and Northern Thailand. Exploratory and confirmatory factor analyses were used to define a measurement model for Logistics 4.0.

Based on 239 responses the exploratory and confirmatory factor analyses resulted in nine items and three factors for the final Logistics 4.0 measurement model. It combines “the leveraging of increased organizational capabilities” (factor 1) with “the rise of interconnection and material flow transparency” (factor 2) and “the setting up of autonomization in logistics processes” (factor 3).

Practitioners can use the proposed measurement model to assess their current level of maturity regarding the implementation of Logistics 4.0 practices. They can map the current state and derive appropriate implementation plans as well as benchmark against best practices across or between industries based on these metrics.

Logistics 4.0 is a relatively young research area, which necessitates greater development through empirical validation. To the best of the authors knowledge, an empirically validated multidimensional construct to measure Logistics 4.0 in manufacturing companies does not exist.

The purpose of this paper is twofold: firstly, to provide a measurement instrument for supply chain 4.0 ambidexterity by applying the theoretical perspective of ambidexterity to advance Industry 4.0; secondly, to empirically analyse how supply chain 4.0 ambidexterity and lean supply chain management contribute to enhancing the focal firm’s operational performance.

Empirical results are obtained through analysis of survey data from a sample of 209 Spanish focal firms in industrial sectors in an intermediate position in the supply chain. Structural equation modelling was performed to test the three proposed hypotheses.

Drawing on resource orchestration theory and the relational resource-based view, this study empirically demonstrates the full mediating role of lean supply chain management in the relationship between supply chain 4.0 ambidexterity and the focal firm’s operational performance.

Although recent research has highlighted the pertinence of applying inter-organisational ambidexterity to foster Industry 4.0 (Hofmann et al., 2019), to the best of the authors’ knowledge, this is one of the first studies to apply this theoretical framework to explain the transition to supply chain 4.0. In addition, to date, to the best of the authors’ knowledge, no study exists that has developed a measurement scale and used this concept in an empirical analysis to advance theory development.