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The purpose of this paper is to investigate how different manufacturing technologies are bundled together and how these bundles influence operations performance and, indirectly, business performance. With the emergence of Industry 4.0 (I4.0) technologies, manufacturing companies can use a wide variety of advanced manufacturing technologies (AMT) to build an efficient and effective production system. Nevertheless, the literature offers little guidance on how these technologies, including novel I4.0 technologies, should be combined in practice and how these combinations might have a different impact on performance.

Using a survey study of 165 manufacturing plants from 11 different countries, we use factor analysis to empirically derive three distinct manufacturing technology bundles and structural equation modeling to quantify their relationship with operations and business performance.

Our findings support an evolutionary rather than a revolutionary perspective. I4.0 technologies build on traditional manufacturing technologies and do not constitute a separate direction that would point towards a fundamental digital transformation of companies within our sample. Performance effects are rather weak: out of the three technology bundles identified, only “automation and robotization” have a positive influence on cost efficiency, while “base technologies” and “data-enabled technologies” do not offer a competitive advantage, neither in terms of cost nor in terms of differentiation. Furthermore, while the business performance impact is positive, it is quite weak, suggesting that financial returns on technology investments might require longer time periods.

Relying on a complementarity approach, our research offers a novel perspective on technology implementation in the I4.0 era by investigating novel and traditional manufacturing technologies together.

Both technological and human-centric perspectives need to be acknowledged when combining lean production practices and Industry 4.0 (I4.0) technologies. This study aims to explore and explain how lean production practices and I4.0 technologies may coexist to enhance the human-centric perspective of manufacturing operations in the era of Industry 5.0 (I5.0).

The research approach is an explorative and longitudinal case study. The qualitative data collection encompasses respondents from different job functions and organizational levels to cover the entire organization. In total, 18 interviews with 19 interviewees and five focus groups with a total of 25 participants are included.

Identified challenges bring forth that manufacturing organizations must have the ability to see beyond lean production philosophy and I4.0 to meet the demand for a human-centric perspective in socially sustainable manufacturing in the era of Industry 5.0.

The study suggests that while lean production practices and I4.0 practices may be considered separately, they need to be integrated as complementary approaches. This underscores the complexity of managing simultaneous organizational changes and new digital initiatives.

The research presented illuminates the elusive phenomena comprising the combined aspects of a human-centric perspective, specifically bringing forth implications for the co-existence of lean production practices and I4.0 technologies, in the transformation towards I5.0.

The study contributes to new avenues of research within the field of socially sustainable manufacturing. The study provides an in-depth analysis of the human-centric perspective when transforming organizations towards Industry 5.0.

The purpose of this paper is to investigate how new technology-based firms (NTBFs) overcome established notions of scale and scope through business model innovation, leveraging the value from digitalization.

The study adopts an exploratory research design, drawing on a novel data set of 50 publicly available interviews with vertical farming (VF) industry leaders and insiders who represent 36 different organizations from North America and Europe.

The study develops a framework showing how NTBFs leverage the value from digitalization via a paradoxical approach combining both efficiency and novelty value drivers.

The study extends current theorizing on the desirability of a combined business model approach leveraging both efficiency and novelty from digitalization. Furthermore, the study is among the first to investigate the unique and highly technological context of VF.

The purpose of this paper is to analyze the business models that 3D printer manufacturers apply to commercialize their technologies. The authors investigate these business models and analyze whether there are business model patterns. The paper describes the gestalt of the business model patterns and discusses differences and similarities.

The authors review the literatures on business models and 3D printing technology. The authors apply a componential business model approach and carry out an in-depth analysis of the business models of 48 3D printer manufacturers in Europe and North America. The authors develop a framework focusing on value proposition, value creation and value capture components. Cluster analysis is used to identify business model patterns.

The results indicate that there are two distinct business model patterns in the industry. The authors termed these patterns the “low-cost online business model” and the “technology expert business model.” The results demonstrate that there is a relationship between business model and technology. The identified patterns are independent of age, company size and country of origin.

The empirical results complement and extend existing literature on business models. The authors contribute to the discussion on business models in the context of novel technology. The technology seems to influence the gestalt of the business model. The sample is limited to European and North American companies and the analysis is based on secondary data.

This is the first empirical study on the business models of 3D printer manufacturers. The authors apply an original mixed-methods approach and develop a framework that can function as a starting point for future research. 3D printer manufacturers can use the identified business model patterns as blueprints to reduce the risk of failure or as a starting point for business model innovation.

Construction 4.0 technology is a novel innovative technology that has been proved to enhance project performance. However, information on the concept's awareness, adoption readiness and challenges in developing economies is still scanty. The purpose of the study is to appraise awareness, adoption readiness and challenges of Construction 4.0 technologies in Nigeria to bring to the fore the state of art of these innovative technologies in the study area.

The study used a convenient sampling technique to select 129 construction professionals (architects, engineers and quantity surveyors) in Osun State, Nigeria, who provided data for the study through a closed-ended structure questionnaire survey. The quantitative data supplied were analysed using frequency, percentile, Cronbach's alpha, mean score (MS) analysis and analysis of variance (ANOVA).

The overall awareness level of construction professionals in the study area about Construction 4.0 technologies is at a moderate level (MS = 3.03). The analysis of each component of the Construction 4.0 technologies shows that BIM (MS = 3.69) has the highest level of awareness, while augmented reality (MS = 2.51) has the least awareness level. More results show a significant difference in the opinion of the respondents, a significant difference in the respondents on 36% of the components of Construction 4.0 technologies. The adoption readiness of the Nigerian construction industry (NCI) to Construction 4.0 technologies is at an initial level (MS = 2.86). However, the 3D printing (MS = 3.36) and augmented reality (MS = 2.49) have the highest and lowest adoption readiness ratings, respectively. There is no significant difference in how respondents ranked the NCI adoption readiness on 73% of the components of Construction 4.0 technologies. The main challenges of Construction 4.0 technologies in the study area are lack of standardisation (MS = 4.02), lack of investment in research and development and cost of implementation (MS = 3.87) each. The result shows that there is perfect unanimity in the way respondents ranked the challenges of Construction 4.0.

The study provided information on the status quo of Construction 4.0 technologies in the NCI to enhance improvement in practice and the attendant project delivery.

The study attempted to bring to the fore the state of the art on awareness, adoption readiness and challenges of Construction 4.0 technologies in Nigeria. The study's information will be valuable to improve project delivery.

The future construction site will be pervasive, context aware and embedded with intelligence. The purpose of this paper is to explore and define the concept of the digital skin of the future smart construction site.

The paper provides a systematic and hierarchical classification of 114 articles from both industry and academia on the digital skin concept and evaluates them. The hierarchical classification is based on application areas relevant to construction, such as augmented reality, building information model-based visualisation, labour tracking, supply chain tracking, safety management, mobile equipment tracking and schedule and progress monitoring. Evaluations of the research papers were conducted based on three pillars: validation of technological feasibility, onsite application and user acceptance testing.

Technologies learned about in the literature review enabled the envisaging of the pervasive construction site of the future. The paper presents scenarios for the future context-aware construction site, including the construction worker, construction procurement management and future real-time safety management systems.

Based on the gaps identified by the review in the body of knowledge and on a broader analysis of technology diffusion, the paper highlights the research challenges to be overcome in the advent of digital skin. The paper recommends that researchers follow a coherent process for smart technology design, development and implementation in order to achieve this vision for the construction industry.

The purpose of this study is to examine the role of artificial intelligence (AI) in transforming the healthcare sector, with a focus on how AI contributes to entrepreneurship and value creation. This study also aims to explore the potential of combining AI with other technologies, such as cloud computing, blockchain, IoMT, additive manufacturing and 5G, in the healthcare industry.

Exploratory qualitative methodology was chosen to analyze 22 case studies from the USA, EU, Asia and South America. The data source was public and specialized podcast platforms.

The findings show that combining technologies can create a competitive advantage for technology entrepreneurs and bring about transitions from simple consumer devices to actionable healthcare applications. The results of this research identified three main entrepreneurship areas: 1. Analytics, including staff reduction, patient prediction and decision support; 2. Security, including protection against cyberattacks and detection of atypical cases; 3. Performance optimization, which, in addition to reducing the time and costs of medical procedures, includes staff training, reducing capital costs and working with new markets.

This study demonstrates how AI can be used with other technologies to cocreate value in the healthcare industry. This study provides a conceptual framework, “AI facilitators – AI achievers,” based on the findings and offer several theoretical contributions to academic literature in technology entrepreneurship and technology management and industry recommendations for practical implication.

This study examines the connection between different digital-twin characteristics and organizational control. Specifically, the study aims to examine whether the digital-twin characteristics exploration, guidance and gamification will affect formal and social control.

The study is based on an analysis of survey results from 139 respondents comprising applied university students who use digital twins.

The results offer an interesting contribution to the literature. The authors consider the digital-twin characteristics exploration, guidance and gamification and investigate their contribution to two types of organizational controls: formal and social. The results show that two characteristics, exploration and gamification, affect the extent to which digital twins can be utilized for social control. Exploration and guidance’s role is significant concerning the extent to which digital twins can be utilized for formal control.

This study contributes to literature by considering multiple digital-twin characteristics and their contribution to two different control outcomes. First, it diverges from previous technical-oriented research by investigating digital twins in a human context. Second, the study is the first to examine digital twins’ effects from an organizational control perspective systematically.

Lean distributed manufacturing (LDM) is being considered as an enabler of achieving sustainability and resilience in manufacturing and supply chain operations. The purpose of this paper is to enhance the understanding of how LDM characteristics affect the resilience of manufacturing companies by drawing upon the experience of food manufacturing companies operating in the UK.

The paper develops a conceptual model to analyse the impact of LDM on the operational resilience of food manufacturing companies. A triangulation research methodology (secondary data analysis, field observations and structured interviews) is used in this study. In a first step, LDM enablers and resilience elements are identified from literature. In a second step, empirical evidence is collected from six food sub-sectors aimed at identifying LDM enablers being practised in companies.

The analysis reveals that LDM enablers can improve the resilience capabilities of manufacturing companies at different stages of resilience action cycle, whereas the application status of different LDM enablers varies in food manufacturing companies. The findings include the development of a conceptual model (based on literature) and a relationship matrix between LDM enablers and resilience elements.

The developed relationship matrix is helpful for food manufacturing companies to assess their resilience capability in terms of LDM characteristics and then formulate action plans to incorporate relevant LDM enablers to enhance operational resilience.

Based on the literature review, no studies exist that investigate the effects of LDM on factory’s resilience, despite many research studies suggesting distributed manufacturing as an enabler of sustainability and resilience.

The purpose of the paper is to explore the meaning of Human Resources (HR) analytics and introduce the HR analytics cycle as a proactive and systematic process for ethically gathering, analyzing, communicating and using evidence-based HR research and analytical insights to help organizations achieve their strategic objectives.

Conceptual review of the current state and meaning of HR analytics. Using the HR analytics cycle as a framework, the authors describe a seven-step process for building evidence-based and ethical HR analytics capabilities.

HR analytics is a nascent discipline and there are a multitude of monikers and competing definitions. With few exceptions, these definitions lack emphasis on evidence-based practice (i.e. the use of scientific research findings in adopting HR practices), ethical practice (i.e. ethically gathering and using HR data and insights) and the role of broader HR research and experimentation. More importantly, there are no practical models or frameworks available to help guide HR leaders and practitioners in doing HR analytics work.

The HR analytics cycle encompasses a broader range of HR analytics practices and data sources including HR research and experimentation in the context of social, behavioral and organizational science.

This paper introduces the HR analytics cycle as a practical seven-step approach for making HR analytics work in organizations.