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The purpose of the study is to investigate the impact of artificial intelligence (AI), machine learning (ML), and data science (DS) on unemployment rates across ten high-income economies from 2015 to 2023.

This study takes a unique approach by employing a dynamic panel data (DPD) model with a generalised method of moments (GMM) estimator to address potential biases. The methodology includes extensive validation through Sargan, Hansen, and Arellano-Bond tests, ensuring the robustness of the results and adding a novel perspective to the field of AI and unemployment dynamics.

The study’s findings are paramount, challenging prevailing concerns in AI, ML, and DS, demonstrating an insignificant impact on unemployment and contradicting common fears of job loss due to these technologies. The analysis also reveals a positive correlation (0.298) between larger government size and higher unemployment, suggesting bureaucratic inefficiencies that may hinder job growth. Conversely, a negative correlation (−0.201) between increased labour productivity and unemployment suggests that technological advancements can promote job creation by enhancing efficiency. These results refute the notion that technology inherently leads to job losses, positioning AI and related technologies as drivers of innovation and expansion within the labour market.

The study’s findings suggest a promising outlook, positioning AI as a catalyst for the expansion and metamorphosis of employment rather than solely a catalyst for automation and job displacement. This insight presents a significant opportunity for AI and related technologies to improve labour markets and strategically mitigate unemployment. To harness the benefits of technological progress effectively, authorities and enterprises must carefully evaluate the balance between government spending and its impact on unemployment. This proposed strategy can potentially reinvent governmental initiatives and stimulate investment in AI, thereby bolstering economic and labour market reliability.

The results provide significant perspectives for policymakers and direct further investigations on the influence of AI on labour markets. The analysis results contradict the common belief of technology job loss. The study’s results are shown to be reliable by the Sargan, Hansen, and Arellano-Bond tests. It adds to the discussion on the role of AI in the future of work, proposing a detailed effect of AI on employment and promoting a strategic method for integrating AI into the labour market.

This study aims to reveal how the organizational structure (vertical integration vs. franchising) of 308 stores in a Spanish fashion retail franchise chain affects their performance measured through two key performance indicators commonly used in this industry, namely, labor productivity and service quality ratings. We also appraise the moderating role played by the servant leadership of franchisees and managers of company-owned outlets to explore its influence on the relationship between organizational structure and store performance.

We have used multivariate analyses to study the research questions, with a panel dataset of quarterly store-level data for the period January–December 2022.

Vertically-integrated stores record lower labor productivity than franchised ones. This impact is lower in stores run by individuals high in servant leadership than in those run by individuals low in it. Franchised outlets also record lower ratings in service quality than vertically-integrated stores, and this negative impact is weaker in stores run by individuals high in servant leadership.

Nothing has thus far been published on the moderating effect of servant leadership in the relationship between the organizational structure of different stores and their outcomes in franchise systems.

The purpose of this paper is to investigate the likelihood of firm exit, focusing on firm- and sector-specific factors and other potential constraints that may lead to exit.

The authors address the main research question by using hazard-cox and probit models on plant level data for the period 1998–1999 to 2012–2013, drawn from the Annual Survey of Industries collected by the Central Statistical Organisation.

The authors find that probability of exit reduces with improved firm performance. Urban firms, proprietary firms and smaller firms are more likely to exit as compared with their respective counterparts. The findings are robust to alternate measures of performance, alternate specifications and different methods.

Studies of entry and exit rates at a point in time are useful in examining the turnover of establishments. But to understand the establishment survival, the authors must also examine the probability of firm exit and the possible determinants that aid exit. There are institutional factors that prevent easy exit of firms from an industry. It would be worthwhile to see how the exit rate will be impacted if these barriers ceased to exist. In this study, the authors construct a model of exit, which would help us to predict firm exit.

The success of construction projects can be indicated by measuring their performance. For effective project performance (PP), the successful execution of a construction projects is very important. A systematic review of the literature on performance areas and performance assessment models was undertaken. The purpose of this paper is to develop a mathematical formulation for construction PP areas to suit the Indian context by modifying the current project quarterback rating (PQR) model.

Based on the literature, the PQR model has not been validated for suitability in the Indian context. To validate the PQR model and modify the same for the Indian context, a survey instrument was used to collect data on performance areas and a multivariate data analysis technique was carried out to develop a modified model. Delphi technique was used to assign the weights for each performance metric in performance areas.

This study concluded the importance of three additional performance areas, namely, productivity, stakeholder satisfaction and environment for assessing PP for Indian construction projects. It also identified the interrelationship between the performance areas and the PP.

The developed modified PQR model (MPQR) will guide the concerned stakeholders to take corrective actions for improving the performance of construction projects.

The MPQR proposed in this paper covers ten areas and is a comprehensive single score that can be used to benchmark and compare performance over different projects to achieve continuous improvement.

This study aims to explore the spatial impact of an increase in the minimum wage on the labor productivity of star-rated hotels in China.

The impact is analyzed by using the dynamic spatial Durbin model.

The authors find a U-shaped link between the increase in minimum wage and labor productivity of star-rated hotels. The long-term impact of a minimum wage increase has a greater influence on labor productivity than its short-term effects. While there is no notable spatial spillover impact observed in the sample of 31 provinces in China, the authors do identify a spatial spillover effect of the minimum wage rises on the labor productivity of star-rated hotels in the central area. Furthermore, they observe heterogeneity across China. The eastern and western regions exhibit a U-shaped relationship, whereas the central region exhibits an inverted U-shaped relationship.

The findings of this study allow government agencies to get a more comprehensive comprehension of the actual consequences of minimum wage hikes on the tourism and hospitality sector, thereby establishing a solid basis for them to develop appropriate policies. Moreover, it offers a variety of suggestions aimed at enhancing the quality and efficiency of hotel management.

Research on the effects of minimum wage standards is scant in the hospitality industry. Based on human capital investment theory, this study examines the effect of the minimum wage standard hikes on labor productivity of star-rated hotels from the spatial perspective, filling the existing research gap.

This study presents the impact of Economic Policy Uncertainty (EPU)-induced Trade Supply Chain Vulnerability (TSCV) on the Small and Medium-Sized Enterprises (SMEs) in India by leveraging the World Bank Enterprise Survey data for 2014 and 2022. Applying econometric techniques, it examines firm size’ influence on productivity and trade participation, providing insights for enhancing SME resilience and trade participation amid uncertainty.

The econometric techniques focus on export participation, along with variables such as total exports, firm size, productivity, and capital intensity. It addresses crucial factors such as the direct import of intermediate goods and foreign ownership. Utilizing the Cobb-Douglas production function, the study estimates Total Factor Productivity, mitigating endogeneity and multicollinearity through a two-stage process. Besides, the study uses a case study of North Indian SMEs engaged in manufacturing activities and their adoption of mitigation strategies to combat unprecedented EPU.

Results reveal that EPU-induced TSCV reduces exports, impacting employment and firm size. Increased productivity, driven by technological adoption, correlates with improved export performance. The study highlights the negative impact of TSCV on trade participation, particularly for smaller Indian firms. Moreover, SMEs implement cost-based, supplier-based, and inventory-based strategies more than technology-based and risk-based strategies.

Policy recommendations include promoting increased imports and inward foreign direct investment to enhance small firms’ trade integration during economic uncertainty. Tailored support for smaller firms, considering their limited capacity, is crucial. Encouraging small firms to engage in international trade and adopting diverse SC mitigation strategies associated with policy uncertainty are vital considerations.

This study explores the impact of EPU-induced TSCV on Indian SMEs’ trade dynamics, offering nuanced insights for policymakers to enhance SME resilience amid uncertainty. The econometric analysis unveils patterns in export behavior, productivity, and factors influencing trade participation during economic uncertainty.

The purpose of this study is to develop a novel general mathematical model to find the optimal product mix of commercial graphite products, which has a complex production process with alternative sub-processes in the graphite mining production process.

The network optimization was adopted to model the complex graphite mining production process through the optimal allocation of raw graphite, byproducts, and saleable products with comparable sub-processes, which has different processing capacities and costs. The model was tested on a selected graphite manufacturing company, and the optimal graphite product mix was determined through the selection of the optimal production process. In addition, sensitivity and scenario analyses were carried out to accommodate uncertainties and to facilitate further managerial decisions.

The selected graphite mining company mines approximately 400 metric tons of raw graphite per month to produce ten types of graphite products. According to the optimum solution obtained, the company should produce only six graphite products to maximize its total profit. In addition, the study demonstrated how to reveal optimum managerial decisions based on optimum solutions.

This study has made a significant contribution to the graphite manufacturing industry by modeling the complex graphite mining production process with a network optimization technique that has yet to be addressed at this level of detail. The sensitivity and scenario analyses support for further managerial decisions.

Digital transformation (DT) is a high-risk, long-term and systematic organizational change, which is highly dependent on the level of operation management. According to the resource-based view and innovation theory, this paper aims to examine the impact of DT on firm performance.

This paper empirically tests the impact of DT on firm performance by selecting total factor productivity and innovation outputs as mediating variables from the perspective of process and outcomes, respectively. It uses Shanghai and Shenzhen A-share-listed companies from 2010–2021 as research samples, searching the frequency of keywords about DT in their annual reports.

The findings reveal the following. First, DT can significantly improve the performance of firms. Second, total factor productivity and innovation outputs play a mediating role between DT and firm performance. Third, the impact of DT on SMEs is more obvious than in bigger ones. However, the effect of DT on performance is more significant in SOEs than non-SOEs. Furthermore, DT positively effects labor-intensive and technology-intensive firms, but negatively effects capital-intensive firms.

This paper first proposes the mechanism analysis from the view of process and outcomes, by using total factor productivity and innovation outputs, which adds depth to the research on the impact of DT on firm performance. Moreover, the authors empirically examine the heterogeneity of the impact of DT on different firm sizes, firm properties and intensity of production factors.

Lean Manufacturing Systems (LMS) gained popularity among manufacturers globally. However, their efficacy in developing and least-developed countries remained noticeably understudied. Motivated by this research gap, the researchers of this study designed a quantitative study with a structured survey technique to investigate its context-specific impact on the apparel industry of a developing country. Hence, this study aimed to examine the relationship between LMS and elimination of waste (EOW) and operational performance (OP) and comprehend how the EOW mediates the relationship between an LMS and OP within the apparel industry of a developing economy.

The researchers collected data from 227 garment companies in Bangladesh. These organization-level data were then analyzed using the structural equation modeling approach with AMOS 20.0 software to examine the direct and indirect effects among EOW, LMS and OP.

The findings of this study suggest that EOW has a direct and significant effect on OP. This research also revealed that EOW has a partial mediating effect on the relationship between LMS and OP.

This research focused on a single industry administering self-reported data and cross-sectional design, limiting generalizability and causal inference.

LMS and directing efforts towards EOW can significantly improve the operational performance of apparel companies by reducing lead times and costs, improving quality and increasing productivity.

These findings can provide useful insight to managers, practitioners and future researchers to understand the relationship between EOW, LMS and OP to optimize their production processes and improve OP in the apparel industry.