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Grounded in the framework of social learning theory (SLT), the current study explores the impact of leaders’ unethical proorganizational behavior (UPB) on their subordinates’ self-management and moral self-efficacy, which, in turn, affect knowledge hiding and sharing among followers. This study aims to examine how instrumental thinking influences the relationship between leader UPB and subordinate behaviors, shaping knowledge sharing and hiding.

Using a longitudinal approach, this research uses a two-wave data collection strategy with a one-month interval. The study cohort comprises 378 employees drawn from technology service firms situated in China.

Empirical findings confirm that leader UPB is linked to increased follower self-management and knowledge hiding, as well as reduced moral self-efficacy. Instrumental thinking moderates these effects, amplifying knowledge hiding and diminishing moral self-efficacy while reducing knowledge sharing.

The study contributes to the existing literature on UPB by offering insights into the distinct consequences of leader UPB on knowledge-related behaviors of followers. Furthermore, the exploration of employees’ instrumental thinking in the context of leader UPB underscores the strategic manipulation of knowledge to fulfill individual goals, thereby enriching the underpinnings of the SLT. The study underscores the imperative for organizations to grasp the implications of UPB and underscores the necessity for stringent ethical frameworks to mitigate its deleterious impact.

The study contributes to the existing literature on UPB by offering insights into the distinct consequences of leader UPB on knowledge-related behaviors of followers. Furthermore, the exploration of employees’ instrumental thinking in the context of leader UPB underscores the strategic manipulation of knowledge to fulfill individual goals, thereby enriching the underpinnings of the SLT. The study underscores the imperative for organizations to grasp the implications of UPB and underscores the necessity for stringent ethical frameworks to mitigate its deleterious impact.

The present study addresses a gap in the current literature by elucidating the multifaceted outcomes of leaders’ UPB on paradoxical behaviors related to knowledge sharing and hiding among followers. This nuanced examination underscores the need to comprehend the intricate contingencies that accentuate the effects of UPB, particularly in the realm of leadership dynamics. Grounded in SLT, this study delves into leadership dynamics and ethical decision-making.

Idea of circular economy defies the classical “make-use-dispose” approach of linear economic model. In the context of health-care industry, it relies heavily on the supply chain practices implemented by industry stakeholders. The purpose of this study is to explore such relationships, study their structure and put it across for attaining sustainability at large.

This study is an empirical research conducted on 145 health-care firms. The collected data is analysed to develop structural and measurement model. The five constructed hypotheses are examined and tested through structural equation modelling.

The study illustrates the latent relationships that exist among the stakeholders involvement, sustainable supply chain practices, sustainable performance and circular economy for health-care industry. It is found that the adoption of sustainable supply chain practices improves health-care performance, which, in turn, have positive influence on circular economy.

The structural and measurement model is developed in the context of circular health-care economy. It can be validated or improvised by conducting similar research in other industry using different methods. This research work fulfils the long existing gap in research by offering a linkage between various constructs to achieve health-care circular economy. Based on the research results, future researchers can build theories of circular economy and sustainability for health-care industry.

The study attempts to study the supply chain ways to achieve circular economy for Indian health-care sector. It considered latent relationships among the set of constructs, which are needed for theory building at later stage.

This paper presents a cross-sectional study that assessed the impact of the COVID-19 pandemic on rural migrants in Bihar. The primary objective of this study was to evaluate the overall impact of the pandemic on migrants and examine their livelihoods, with a focus on identifying measures that can mitigate the economic consequences.

This study used a telephonic survey to collect primary data from 419 respondents. Descriptive statistics were used to analyze the data, and three indices were constructed: fear and worries, trust and prevention.

The findings provide insights into the psychological well-being of migrant workers and highlight the challenges they face in sustaining their livelihoods amidst the pandemic. This study concludes by suggesting potential measures to alleviate the economic impact and enhance the resilience of this vulnerable population.

This study may be limited by the representativeness of the sample as well as the potential for social desirability bias. The study may also be limited by the reliability and validity of the measures used to capture the fear and worries, trust and prevention indices.

Numerous studies have examined the impact of the COVID-19 pandemic on rural migrants. However, there are limited studies that estimate the impact of the proposed study based on the challenges faced by rural migrants in Bihar during the pandemic.

The agriculture sector is crucial for all economies, especially the developing ones. However, agricultural production is influenced by government intervention, which outshines the significant role of good governance indicators in agricultural productivity. In addition to this, the major climate changes also posed various challenges and led to water shortages and yield losses. Thus affecting agricultural production. In this paper, we address the issue by determining the association between state governance and agricultural productivity in N-11 countries.

Panel data have been collected from 2000 to 2021 through the Governance Indicator, World Development Indicator and World Bank databases. For data analysis, the researcher has utilized the autoregressive distributed lag (ARDL) estimations.

Through ARDL estimations, it is suggested that corruption (CC), employment in agriculture (EAG), political stability and violence absence (PS), rule of law (RL), regulatory equality (RQ) and water quality (WQ) significantly impact agricultural productivity (AGP) in the long run. In the short run, the impact of RL on AGP has been significant.

This study follows the method of data collection from secondary sources, which hinders the effectiveness of this study as, on the basis of the respective data, the potential of the researcher to get specific answers to research questions has been affected. Also, this study examines the context of N-11 countries from 2000 to 2021, which exerts a geographical limitation. While exploring the association between state governance and agricultural productivity, this study neglects the internal aspects of implementing state policies in firms.

On practical grounds, the significant association demonstrated by this study encourages agricultural firms to keenly consider state policies to gain sustainable agricultural development. Moreover, this study encourages agricultural firms to efficiently follow governance policies for efficient productivity. The outcomes of the study have shown that agricultural employment and governance infrastructure can efficiently enhance agricultural productivity. Besides, as per the results, water quality also positively impacts agricultural productivity; thus, relevant steps can be taken by the agricultural sector to improve the quality of water.

The purpose of this article is to investigate and analyze the interactions between economic and sustainable development elements in the context of remanufacturing in India.

To comprehend the hierarchical and contextual link among factors impacting remanufacturing in India, the study used interpretive structural modeling (ISM) and decision-making trial and evaluation laboratory (DEMATEL) methodologies.

The integrated ISM-DEMATEL approach identifies optimal utilization of the resources as the most crucial factor influencing remanufacturing in India, followed by reducing landfills, conserving energy and low cost. The study also reveals that optimal utilization of resources, reduction of landfills, conservation of energy and incorporated advanced technology impacts most of the factors but get affected by a few factors.

Industry practitioners and policymakers should consider the remanufacturing process to achieve sustainable and economic development. The government and other stakeholders can use the ISM framework and cause-and-effect diagram to classify the impact factors and their impact on the Indian economy and environment.

This study supports the process to save the landfills and curbing pollution, conserve energy and optimize utilization of the resources, generate employment and supporting the development of the economy. Remanufacturing will undoubtedly contribute to the development of an environment and economy in India that benefits both.

ISM and DEMATELs strategy offers a tiered model and a cause-and-effect relationship between the variables affecting remanufacturing in India.

Fused Filament Fabrication (FFF) is one of the growing technologies in additive manufacturing, that can be used in a number of applications. In this method, process parameters can be customized and their simultaneous variation has conflicting impacts on various properties of printed parts such as dimensional accuracy (DA) and surface finish. These properties could be improved by optimizing the values of these parameters.

In this paper, four process parameters, namely, print speed, build orientation, raster width, and layer height which are referred to as “input variables” were investigated. The conflicting influence of their simultaneous variations on the DA of printed parts was investigated and predicated. To achieve this goal, a hybrid Genetic Algorithm – Artificial Neural Network (GA-ANN) model, was developed in C#.net, and three geometries, namely, U-shape, cube and cylinder were selected. To investigate the DA of printed parts, samples were printed with a central through hole. Design of Experiments (DoE), specifically the Rotational Central Composite Design method was adopted to establish the number of parts to be printed (30 for each selected geometry) and also the value of each input process parameter. The dimensions of printed parts were accurately measured by a shadowgraph and were used as an input data set for the training phase of the developed ANN to predict the behavior of process parameters. Then the predicted values were used as input to the Desirability Function tool which resulted in a mathematical model that optimizes the input process variables for selected geometries. The mean square error of 0.0528 was achieved, which is indicative of the accuracy of the developed model.

The results showed that print speed is the most dominant input variable compared to others, and by increasing its value, considerable variations resulted in DA. The inaccuracy increased, especially with parts of circular cross section. In addition, if there is no need to print parts in vertical position, the build orientation should be set at 0° to achieve the highest DA. Finally, optimized values of raster width and layer height improved the DA especially when the print speed was set at a high value.

By using ANN, it is possible to investigate the impact of simultaneous variations of FFF machines’ input process parameters on the DA of printed parts. By their optimization, parts of highly accurate dimensions could be printed. These findings will be of significant value to those industries that need to produce parts of high DA on FFF machines.

The purpose of this study, most recent advancements in threedimensional (3D) printing have focused on the fabrication of components. It is typical to use different print settings, such as raster angle, infill and orientation to improve the 3D component qualities while fabricating the sample using a 3D printer. However, the influence of these factors on the characteristics of the 3D parts has not been well explored. Owing to the effect of the different print parameters in fused deposition modeling (FDM) technology, it is necessary to evaluate the strength of the parts manufactured using 3D printing technology.

In this study, the effect of three print parameters − raster angle, build orientation and infill − on the tensile characteristics of 3D-printed components made of three distinct materials − acrylonitrile styrene acrylate (ASA), polycarbonate ABS (PC-ABS) and ULTEM-9085 − was investigated. A variety of test items were created using a commercially accessible 3D printer in various configurations, including raster angle (0°, 45°), (0°, 90°), (45°, −45°), (45°, 90°), infill density (solid, sparse, sparse double dense) and orientation (flat, on-edge).

The outcome shows that variations in tensile strength and force are brought on by the effects of various printing conditions. In all possible combinations of the print settings, ULTEM 9085 material has a higher tensile strength than ASA and PC-ABS materials. ULTEM 9085 material’s on-edge orientation, sparse infill, and raster angle of (0°, −45°) resulted in the greatest overall tensile strength of 73.72 MPa. The highest load-bearing strength of ULTEM material was attained with the same procedure, measuring at 2,932 N. The tensile strength of the materials is higher in the on-edge orientation than in the flat orientation. The tensile strength of all three materials is highest for solid infill with a flat orientation and a raster angle of (45°, −45°). All three materials show higher tensile strength with a raster angle of (45°, −45°) compared to other angles. The sparse double-dense material promotes stronger tensile properties than sparse infill. Thus, the strength of additive components is influenced by the combination of selected print parameters. As a result, these factors interact with one another to produce a high-quality product.

The outcomes of this study can serve as a reference point for researchers, manufacturers and users of 3D-printed polymer material (PC-ABS, ASA, ULTEM 9085) components seeking to optimize FDM printing parameters for tensile strength and/or identify materials suitable for intended tensile characteristics.

This study aims to propose and develop a new digital collaborative supply chain (CSC) model completely based on the emerging Industry 4.0 technologies. The digital model aims to support the main factors likely to affect CSC. This proposed model combines the most well-known digital tools such as blockchain technology, Internet of Things (IoT) and cloud computing (CC).

Motivated by its effective solution to enhance trust, traceability, transparency and minimize costs and risks, the combination of the most well-known digital tools such as blockchain technology, IoT and CC to develop a new digital CSC model is addressed in this research. This study first investigates and conducts a deep review analysis that explores how Industry 4.0 technologies can enable collaboration mechanisms. Second, based on an analysis of literature review, the main factors likely to affect CSC have been identified and analysed. Finally, the authors combine digital tools to support the identified factors to enhance transparency, traceability and trust by proposing a new digital CSC model. This proposed model will be used as a referential guide to encourage and motivate SC actors to collaborate in digital CSC.

This work provides many important contributions to theory and practice. First, role and impacts of the most well-known digital tools such as blockchain technology, IoT and CC for digitizing CSC have separately presented and developed. Second, the authors conceptualized a framework by developing a new digital CSC model. This conceptual digital model can be used as a referential guide for all SC actors in order to motivate them to collaborate in a modern, intelligent, secure and reliable SC. It can also support all factors affecting CSC.

The originality of this study is first investigating separately the roles and impacts of each digital tool on CSC performance. Second, the authors combine the most well-known digital tools such as blockchain technology, IoT and CC in order to develop an efficient, smart, modern and new digital CSC model. In this combination, CC is used as platform as a service enabling to link and connect the blockchain and IoT to support the main factors affecting CSC. Unlike to digital CSC model with only one digital tool, the proposed model is more realistic since depending on the information to be shared with other actors, the most appropriate tool will be automatically detected and used. This solution offers a large choice to SC actors for real time data and information sharing. In addition, the proposed model will largely enhance traceability, transparency and trust in CSC.