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This paper aims to reveal the mechanism for improving ductile machinability of 3C-silicon carbide (SiC) and associated cutting mechanism in stress-assisted nanometric cutting.

Molecular dynamics simulation of nano-cutting 3C-SiC is carried out in this paper. The following two scenarios are considered: normal nanometric cutting of 3C-SiC; and stress-assisted nanometric cutting of 3C-SiC for comparison. Chip formation, phase transformation, dislocation activities and shear strain during nanometric cutting are analyzed.

Negative rake angle can produce necessary hydrostatic stress to achieve ductile removal by the extrusion in ductile regime machining. In ductile-brittle transition, deformation mechanism of 3C-SiC is combination of plastic deformation dominated by dislocation activities and localization of shear deformation. When cutting depth is greater than 10 nm, material removal is mainly achieved by shear. Stress-assisted machining can lead to better quality of machined surface. However, there is a threshold for the applied stress to fully gain advantages offered by stress-assisted machining. Stress-assisted machining further enhances plastic deformation ability through the active dislocations’ movements.

This work describes a stress-assisted machining method for improving the surface quality, which could improve 3C-SiC ductile machining ability.

The purpose of this paper is to investigate the cutting mechanism of drilling printed circuit board (PCB) and to optimize the drilling parameters for decreasing burr size and thrust force.

An experimental study was carried out to investigate the effect of drilling parameters on thrust force and burr formation. The drilling process of PCB was divided by the variation of drilling force signals. Analysis of variance (ANVONA) was carried out for burr size and thrust force. Desirability function method was used in multiple response optimization, to find the best drilling parameters.

Enter burr and exit burr have different morphologies and types. The generation of enter burr is mainly caused by burr bending which can be observed in micrographs, whereas the generation of exit burr is more complicated than enter burr; both burr breakup and burr bending are observed in exit burrs. In the selected area, the optimized spindle speed and feed rate for drilling PCB is 12 krev/min and 6 mm/s, respectively.

In this paper, hole wall roughness and tool wear were not considered in the optimization of drilling parameters. The future research work should consider them.

This paper investigates the mechanism of burr formation and thrust force in drilling PCB and then optimizes the drilling parameters to decrease the burr formation and thrust force.

Big data and predictive analytics (BDPA) has received great attention in terms of its role in making business decisions. However, current knowledge on BDPA regarding how it might link organizational capabilities and organizational performance (OP) remains unclear. Drawing from the resource-based view, the purpose of this paper is to propose a model to examine how information technology (IT) deployment (i.e. strategic IT flexibility, business–BDPA partnership and business–BDPA alignment) and HR capabilities affect OP through BDPA.

To test the proposed hypotheses, structural equation modeling is applied on survey data collected from 159 Indian firms.

The results show that BDPA diffusion mediates the influence of IT deployment and HR capabilities on OP. In addition, there is a direct effect of IT deployment and HR capabilities on BDPA diffusion, which also has a direct relationship with OP.

Through this study, authors demonstrate that IT deployment and HR capabilities have an indirect impact on OP through BDPA diffusion.

The purpose of paper is twofold. First, it provides a consolidated overview of the existing literature on “big data” and second, it presents the current trends and opens up various future directions for researchers who wish to explore and contribute in this rapidly evolving field.

To achieve the objective of this study, the bibliographic and network techniques of citation and co-citation analysis was adopted. This analysis involved an assessment of 57 articles published over a period of five years (2011-2015) in ten selected journals.

The findings reveal that the number of articles devoted to the study of “big data” has increased rapidly in recent years. Moreover, the study identifies some of the most influential articles of this area. Finally, the paper highlights the new trends and discusses the challenges associated with big data.

This study focusses only on big data concepts, trends, and challenges and excludes research on its analytics. Thus, researchers may explore and extend this area of research.

To the knowledge of the authors, this is the first study to review the literature on big data by using citation and co-citation analysis.

This paper aims to present an experimental investigation of improving the surface corrosion resistance of sintered neodymium-iron-boron (NdFeB) magnets by electrical discharge machining (EDM) in different dielectric fluids.

Scanning electron microscope and X-ray diffraction were used to analyze the surface morphology and chemical structure of recast layers formed by EDM using kerosene and distilled water as the dielectric fluids. Polarization scans and electrochemical impedance spectroscopy were applied to investigate the post-machining corrosion resistance.

The test results indicated that the recast layer produced during EDM had amorphous characteristics, and the newly formed amorphous structure could improve the corrosion resistance of the NdFeB material. The corrosion resistance of the recast layer formed in kerosene was better than that formed in distilled water.

Surface corrosion modification of sintered NdFeB magnets by means of electrical discharge with an ordinary copper electrode is proposed in this paper. The layer formed by EDM exhibits different behavior to that of the interior of the bulk material and improves the anti-corrosion performance of NdFeB magnets.

The study investigates the effect of technological capabilities, organizational capabilities and environmental capabilities on Logistics 4.0 capabilities and also examines the effect of Logistics 4.0 capabilities on firm performance.

The proposed theoretical framework is tested using WarpPLS 6.0 software. We selected samples from the Automotive Component and Allied Manufacturers in South Africa. Initially, we sent the structured questionnaire online using Google forms to 800 potential respondents. After doing follow ups, we received 230 completed survey responses. Further, data preparation is done using established scientific approach and we checked suitability of its use in structural equation modelling. After ensuring all necessary checks are completed, the results are found satisfactory to further proceed with testing of research hypotheses.

It is observed that technological capabilities, organizational capabilities and environmental capabilities show significant effect on Logistics 4.0 capabilities. However, the outcome of technological capabilities and environmental capabilities on Logistics 4.0 capabilities (ß = 0.27) is found stronger than organizational capabilities. Logistics 4.0 capabilities shows significant effect on firm performance.

It is important that the sustainability goals are aligned with Logistics 4.0 strategies. Managers need to increase focus towards development of Logistics 4.0 dynamic capabilities that enhance agility and responsiveness in the supply chain. Managers should check the financial performance and market conditions continuously to further review logistics performance as this can influence the overall firm performance.

This study advances the literature on Logistics 4.0 applications in operations management by investigating the key links such as Logistics 4.0 capability development and firm performance.

Technological developments have made it possible for organizations to use enterprise resource planning (ERP) services without indulging in heavy investments like IT infrastructure, trained manpower for implementation and maintenance and updating the systems regularly to maintain business competitiveness. Plug and play model offered by cloud ERP has led to a constant creation of large data sets which are structured, semi-structured and unstructured by nature. Thus, there has been a need to analyze such complex data sets and the purpose of this paper is to focus on how cloud ERP and big data predictive analytics (BDPA) will impact the performance of a firm.

A dynamic capability view (DCV) theory-based model was developed and the authors have collected data by using an online questionnaire from India. Thereafter, the authors have analyzed it by employing structural equation modeling.

SEM analysis of 231 respondents showcases that the use of DCV theory to define the relationships of cloud ERP and BDPA has been the right move. Out of the 13 hypotheses empirically tested, only 7 hypotheses were supported by the data.

The study showcases cross-sectional data from India. It would be interesting for this study to see if the country-level differences would influence these relationships between cloud ERP and financial performance, BDPA and financial performance and cloud ERP and BDPA.

This study empirically tests the relationship of cloud ERP and BDPA through a model based on DCV theory.

The purpose of this paper is to understand how big data and predictive analytics (BDPA), as an organizational capability, can improve both visibility and coordination in humanitarian supply chains.

The authors conceptualize a research model grounded in contingent resource-based view where the authors propose that BDPA capabilities affect visibility and coordination under the moderating effect of swift trust. Using ordinary least squares regression, the authors test the hypotheses using survey data collected from informants at 205 international non-government organizations.

The results indicate that BDPA has a significant influence on visibility and coordination. Further, the results suggest that swift trust does not have an amplifying effect on the relationships between BDPA and visibility and coordination. However, the mediation test suggests that swift trust acts as a mediating construct. Hence, the authors argue that swift trust is not the condition for improving coordination among the actors in humanitarian supply chains.

The major limitation of the study is that the authors have used cross-sectional survey data to test the research hypotheses. Following Guide and Ketokivi (2015), the authors present arguments on how to address the limitations of cross-sectional data or use of longitudinal data that can address common method bias or endogeneity-related problems.

Managers can use this framework to understand: first, how organizational resources can be used to create BDPA, and second, how BDPA can help build swift trust and be used to improve visibility and coordination in the humanitarian supply chain.

This is the first research that has empirically tested the anecdotal and conceptual evidence. The findings make notable contributions to existing humanitarian supply chain literature and may be useful to managers who are contemplating the use of BDPA to improve disaster-relief-related activities.

This study aims to examine the farmers’ awareness level and explores the factors, which may influence their adoption intention regarding solar powered pumps.

The study consist of a sample of 510 respondents selected from the rural region of Punjab (India) by using convenience sampling. Descriptive analysis, exploratory factor analysis, confirmatory factor analysis and multiple regression analysis techniques have been used for the analytical purpose.

The study reveals that dimensions such as perceived benefit, perceived compatibility and government incentives have a significant impact on intention to use solar powered pumps, whereas high investment cost and lack of awareness regarding government subsidies are the main reason for non-adoption of the same.

The sample size has been selected on the basis of convenience sampling and has been taken from the rural area, which may affect its generalizability.

The present research is expected to be useful for the manufacturers, regulators, customers, commercial banks, product and service providers, and other environmental institutions.

The study has acknowledged various intentional factors, which influence the adoption decision of solar powered pumps. Therefore, the present study will be useful to formulate action plans to improve the environmental quality.

Adoption of solar energy plays an important role in the growth of a country. There are many factors which influence the adoption of solar energy in India. The study is designed to identify factors that determine the acceptance or rejection of solar energy systems in India.

Relationship among identified variables is established through interpretive structural modelling (ISM) and thus a conceptually validated model is evolved. Further, MICMAC analysis is conducted to understand the driving power and dependence of these variables.

It is revealed that experience and habit, awareness and social influence are the intermediary variables. MICMAC Analysis shows that no variable is disconnected from the system and all the variables influence the adoption of solar energy in India.

The present study is expected to be useful to decision makers, end users and research organisations related to solar energy adoption.

Various intentional factors influencing solar energy systems adoption have been acknowledged in the present study, thus making it useful for formulation of action plans and enhance the usage of solar energy systems to improve environment quality.