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The purpose of this paper is to propose and develop PA2200-based composite powder containing 0-15 Wt.% magnesium oxide before directly using it in selective laser sintering (SLS) machine to produce end-use products for low-volume production in the engineering applications with keen focus to meet the functional requirements which rely on material properties.

The methodology reported emphasises PA2200-based composite powder containing 0-15 Wt.% magnesium oxide development for SLS process which starts with preparation and characterisation of composite material, thermal and rheological study of composite material to decide optimum process parameters for SLS process machine to get optimal part properties. Further, to verify composite material properties, a conventional casting methodology is used. The composition of composite materials those possessing good properties are further selected for processing in SLS process under optimal processing parameters.

The process parameters of SLS machine are material-dependent. The effect of temperature in X-ray diffraction profile is negligible in the case of magnesium oxide reinforced PA2200 composite material. The cyclic heating of material increases melting point temperature, this grounds to modify part bed temperature of material every time before processing on SLS machine to uphold build part properties, as well as material. With the rise in temperature, the Melt flow index and rheological property of materials change. The magnesium oxide reinforced PA2200 composite material has high thermal stability than pure PA2200 material. By the addition of small quantity of magnesium oxide, most of the mechanical property and flammability property improves while elongation at break (percentage) decreases significantly.

The proposed PA2200-based composite powder containing 0-15 Wt.% magnesium oxide material development system and casting metrology to verify developed material properties will be very useful to develop new composite material for SLS process with use of less material. The developed methodology has proven, especially in the case where non-experts or student need to develop composite material for SLS process according to the property requirement of applications.

Unlike earlier composite material development methodology, the projected methodology of polymer-based composite material and confirmation of material properties instead of commencing SLS process provides straight forward means for SLS process composite materials development with less use of the material and period of time.

This study proposes a methodology based on data source triangulation to measure the “strategic fit” for the automotive supply chain.

At first, the authors measured the responsiveness of the Indian automobile supply chain, encompassing the top ten major automobile manufacturers, using both sentiment and conjoint analysis. Second, the authors used data envelopment analysis to identify the frontiers of their supply chain. The authors also measured the supply chain's efficiency, using the balance sheet. Further, the authors analyzed the “strategic fit” zone and discussed the results.

The results indicate that both the proposed methods yield similar outcomes in terms of strategic fitment.

The study outcomes facilitate measuring the strategic fit, thereby leveraging the resources available to align. The methodology proposed is both easy to use and practice. The methodology eases time and costs by eliminating hiring agencies to appraise the strategic fit. This valuable method to measure strategic fit can be considered feedback for strategic actions. This methodology could also be incorporated possibly as an operative measurement and control tool.

Data triangulation meaningfully enhances the accuracy and reliability of the analyses of strategic fit. Data triangulation leads to actionable insights relevant to top managers and strategic positioning of top managers within a supply chain.

The changes in tensile behavior of polycrystalline nanocopper lattice with changes in temperature, average grain size (AGS) and strain rate, have been explored. The existence of a critical AGS has also been observed which shows that the Hall–Petch relationship behaves inversely.

Nanoscale deformation of polycrystalline nanocopper has been done in this study with the help of an embedded atom method (EAM) potential. Voronoi construction method has been employed for creating four polycrystals of nanocopper with different sizes. Statistical analysis has been used to examine the observations with emphasis on the polycrystal size effect on melting point temperature.

The study has found that the key stress values (i.e. elastic modulus, yield stress and ultimate tensile stress) are significantly influenced by the considered parameters. The increase in strain rate is observed to have an increasing impact on mechanical properties, whereas the increase in temperature degrades the mechanical properties. In-depth analysis of the deformation mechanism has been studied to deliver real-time visualization of grain boundary motion.

This study provides the relationship between required grain size variations for consecutive possible variations in mechanical properties and may help to reduce the trial processes in the synthesis of polycrystalline copper based on different temperatures and strain rates.

This paper aims to propose a decision-making framework by investigating the impact of perceived risk and computer self-efficacy on the intention to use online stock trading. Furthermore, it demonstrates the mediation effect of attitude and perceived risk as well as the moderating effect of financial literacy.

An integration of two popular models, technology acceptance model (TAM) and theory of planned behaviour (TPB), is used to provide a sound theoretical base and enhance the understanding of investors’ behaviour towards online trading platforms. The proposed hypothesised model was examined using structural equation modelling.

The results obtained from this study indicate that all variables, except subjective norms, had a significant impact on investors’ intention to trade online. Perceived risk was found to be a partial mediator between computer self-efficacy and the intention of investors. Finally, financial literacy was also found as a significant moderator for online trading intention of investors.

This study shows the significance of using the TAM and TPB together to provide a comprehensive understanding of the factors that influence an investor’s behaviour in adopting and using technology for online trading. The hybrid approach of TAM and TPB could be considered for a more nuanced and complete understanding of technology adoption and usage in risky affairs like investment decisions. Again, the significant moderating role of financial literacy provides a lance to look into the scope for improvements in investment decision-makings.

The paper develops an assessment framework for analysing the variables based on the hybrid approach for online trading intention in the context of a developing country.

This paper proposes a decision-making framework for assessing various dimensions and barriers that have affected the admission process in management educational institutions during the ongoing pandemic. The framework considers the interrelationship between the obstacles and highlights the importance of each barrier.

An integrated method based on decision-making trial and evaluation laboratory and analytical network process is proposed to structure the barrier assessment framework. Results obtained from the study are validated by comparing them against the conventional analytical hierarchy process.

The results obtained from this study indicate four significant dimensions that hinder admission in Indian management institutes, namely, governmental, financial, sectoral, institutional and market. The top five barriers are demand shift towards technical (alternative) skills, acceptance of the graduated students, lack of industry–institute collaboration, lack of long-term vision and opening new Indian Institute of Technologies (IITs) and Indian Institute of Managements (IIMs).

During this ongoing pandemic, many educational institutes have been forced to shift from the traditional classroom to a virtual teaching model. In this regard, this study helps identify and assess the barriers to admission in Indian management institutes during this epidemic and thus, contribute to the literature. The findings will assist all stakeholders and policymakers of management institutions design and develop appropriate managerial strategies. The study is conducted in the Indian management educational institute context and can be extended to technical education institutions for deeper insights.

The paper develops an assessment framework for analysing the barriers to admission in Indian management institutes during the ongoing COVID-19 pandemic. Research implications are discussed in the context of a developing country.

This paper aims to consolidate the results of various researchers focusing the different applications, so that this paper could become the torch bearer for the futuristic researchers working in the domain of cold gas dynamics spray coating.

A study on the cold spray coating is presented by summarizing the data present in literature. Important factors such as coating temperature, pressure, coating thickness, particle size, which affect the erosion-corrosion (E-C) resistance, physical and mechanical properties of boiler steel are stated. This paper also addresses the use of cold spray coating and compares it with other different thermal spray processes.

From the literature review, it was noticed that cold spray technology is best as compare to other thermal spray processes to reduce porosity, increase hardness, adhesion strength and retention in properties of feedstock powders.

Cold spray coating technology has a great potential in almost every field especially in restoration of surfaces, generation of complex surface, biomedical application, resist hot corrosion, wear, oxidation and erosion corrosion.

The case study “Maruti Suzuki – toward cleaner mobility” has been written keeping in view the requirements in the field of strategic management. The key learning objectives are as follows:• Analysis of business environment.• Product development strategy – creating market segment to gain competitive advantage by leveraging available organizational capabilities.• Strategic decision-making – understanding strategic decision-making process in a complex and highly competitive business scenario.

Maruti Suzuki, a leader in Indian automotive market with around 50% market share in passenger cars, was likely to face intense competition because of disruption by electric vehicles. As electric vehicles adoption was increasing globally in developed countries, automotive companies shaped their strategy accordingly to stay relevant. Maruti Suzuki was yet to be ready with electric vehicles and approached this space differently than other competitors. However, with Indian Government pushing toward cleaner mobility, it was yet to be seen how the company would manage to comply with legislations and compete effectively in marketplace. Indian Auto major, Maruti Suzuki, was on the edge to decide future strategy on electric vehicles to sustain its leadership position. The Indian automotive sector was going through the transformation where auto original equipment manufacturers were bringing electric vehicles and supporting policies from government likely to accelerate its adoption. Maruti Suzuki was striving to counter the competition with available resources to create competitive advantage in changing environment and continue to remain profitable with leadership position in Indian automotive market. The company had successfully maintained its leading position over three decades and transformed the automotive space with its strategies ahead of the curve. Now the company was standing at crossroads with regard to future technology on cleaner mobility. Mr Bhargava had to decide whether to throw the hat in EV ring or wait for other alternate technology disruption.

Management studies and executive development programs.

Teaching notes are available for educators only.

CSS 11: Strategy

Business models (BMs) are becoming increasingly crucial for value creation in the healthcare sector. The study explores the conceptualization and application of BM concepts within the healthcare sector and investigates their evolution in emerging economies (EEs) and developed economies (DEs). This study aims to uncover these two contexts' shared characteristics and unique variances through a comparative analysis.

The paper systematically investigates and consolidates the literature on healthcare by employing the antecedents, decisions and outcomes (ADO) framework and finally examines 71 shortlisted articles published between 2003 and 2022.

The recognition of the BM within healthcare is increasing, both in EEs and DEs. EEs prioritize value creation and capture through cost efficiency, while DEs focus on innovation. Key theories employed include a resource-based view, the network theory and the theory of innovation. Case studies are commonly used as a methodology. Further research is needed to explore the decisions and outcomes of BMs.

The study adopts stringent filtration and keyword criteria, potentially excluding relevant research. Future researchers are encouraged to broaden their selection criteria to encompass a more extensive range of relevant studies.

Beyond comparing and highlighting gaps in BMs between EEs and DEs, benchmarking DE's healthcare business models (HBMs) helps healthcare organizations in EEs align their practices, mitigate risks and establish efficient healthcare systems tailored to their specific contexts. The study adopts stringent filtration and keyword criteria, potentially excluding relevant research. Future researchers are encouraged to broaden their selection criteria to encompass a more extensive range of relevant studies.

The study analyzes HBMs using an SLR framework perspective and provides practical implications for academicians and practitioners to enhance their decision-making.

The use of social media is becoming increasingly important for entrepreneurial marketing as a way to gain psychological empowerment through female entrepreneurship. The participation of female entrepreneurs on social media has witnessed an increasing trend. The purpose of this study is to understand the impact of social media participation on female entrepreneurs towards digital entrepreneurship intention and their psychological empowerment.

This cross-sectional study integrates the Stimulus–Organism–Response framework with uses and gratification theory to understand and determine a theoretical framework in understanding the importance of social media for female entrepreneurship in the contemporary digital era. To ensure internal consistency of the latent constructs, this study determines Cronbach’s alpha for all the variables. Further, exploratory factor analysis is performed to ensure the unidimensionality of the latent constructs. Structural equation modelling is performed to test the theoretical framework.

Data analysis confirms the significant effect of social media participations on female entrepreneurs towards their digital entrepreneurship intention which further affects their psychological empowerment.

The practical contributions of this study highlight the importance of female entrepreneurship which is essential for attaining self-reliance by reducing the socio-economic barriers. Further, female entrepreneurs’ participation in social media communities enhances the levels of empowerment.

Deep learning techniques are unavoidable in a variety of domains such as health care, computer vision, cyber-security and so on. These algorithms demand high data transfers but require bottlenecks in achieving the high speed and low latency synchronization while being implemented in the real hardware architectures. Though direct memory access controller (DMAC) has gained a brighter light of research for achieving bulk data transfers, existing direct memory access (DMA) systems continue to face the challenges of achieving high-speed communication. The purpose of this study is to develop an adaptive-configured DMA architecture for bulk data transfer with high throughput and less time-delayed computation.

The proposed methodology consists of a heterogeneous computing system integrated with specialized hardware and software. For the hardware, the authors propose an field programmable gate array (FPGA)-based DMAC, which transfers the data to the graphics processing unit (GPU) using PCI-Express. The workload characterization technique is designed using Python software and is implementable for the advanced risk machine Cortex architecture with a suitable communication interface. This module offloads the input streams of data to the FPGA and initiates the FPGA for the control flow of data to the GPU that can achieve efficient processing.

This paper presents an evaluation of a configurable workload-based DMA controller for collecting the data from the input devices and concurrently applying it to the GPU architecture, bypassing the hardware and software extraneous copies and bottlenecks via PCI Express. It also investigates the usage of adaptive DMA memory buffer allocation and workload characterization techniques. The proposed DMA architecture is compared with the other existing DMA architectures in which the performance of the proposed DMAC outperforms traditional DMA by achieving 96% throughput and 50% less latency synchronization.

The proposed gated recurrent unit has produced 95.6% accuracy in characterization of the workloads into heavy, medium and normal. The proposed model has outperformed the other algorithms and proves its strength for workload characterization.