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This paper aims to empirically analyze the impact of export diversification on gross domestic product (GDP) per capita growth.

Using system generalized method of moments (GMM), a nonlinear model in a dynamic panel data growth framework for 101 countries between 1995 and 2019 was estimated.

Results evidenced that export concentration, measured by the Herfindahl–Hirschman Index (HHI), is negatively associated with GDP per capita growth after controlling for the effects of other explanatory variables. Further, the squared term of HHI used in the model to measure the nonlinear relationship between export concentration and economic growth indicated that the low-income and lower-middle-income countries benefited from export diversification. At the same time, high-income and upper-middle-income countries perform well with their export specialization. The results of the robustness check validate the findings of nonlinear estimation.

The findings recommend that low-income and lower-middle-income countries diversify their export basket to improve economic growth by generating stable export earnings. Similarly, high-income and upper-middle-income countries should focus on measures to close the product lines which no longer belong to their factor endowments and rebalance their export basket.

This study contributes to the existing literature by using the system GMM method, which is most appropriate for a dynamic panel data growth framework with up-to-date data. Further, this study segregates a large panel into 43 concentrated and 58 diversified countries to test the robustness of the empirical results.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

A transformative service aims to improve wellbeing; however, current approaches have an implicit assumption that all wellbeing dimensions are equal and more dimensions led to higher wellbeing. The purpose of this paper is to present evidence for a new framework that identifies the paradox of competing wellbeing dimensions for both the individual and others in society – the transformative service paradox (TSP).

Data is drawn from a mixed-method approach using qualitative (interviews) and quantitative data (lab experiment) in an electricity service context. The first study involves 45 household interviews (n = 118) and deals with the nature of trade-offs at the individual level to establish the concept of the TSP. The second study uses a behavioral economics laboratory experiment (n = 110) to test the self vs. other nature of the trade-off in day-to-day use of electricity.

The interviews and experiment identified that temporal (now vs. future) and beneficiary-level factors explain why individuals make wellbeing trade-offs for the transformative service of electricity. The laboratory experiment showed that when the future implication of the trade-off is made salient, consumers are more willing to forego physical wellbeing for environmental wellbeing, whereas when the “now” implication is more salient consumers forego financial wellbeing for physical wellbeing.

This research introduces the term “Transformative Service Paradox” and identifies two factors that explain why consumers make wellbeing trade-offs at the individual level and at the societal level; temporal (now vs. future) and wellbeing beneficiary.