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This study, conducted in a large public sector undertaking in India that had undergone a major change recently, examines the influence of personality factors on employees ability of coping with organizational change in the Indian context. The role of personality factors—self‐esteem, optimism, perceived control, and change self‐efficacy—on influencing employees for coping with organizational change has been examined. Further, the role of demographic variables on the employee's ability of coping with organizational change has also been studied. Results that these personality factors—perceived control, optimism and change self‐efficacy—indicate a significant relationship with coping with organizational change. Contrary to expectations, self‐esteem has not been significantly associated with coping with organizational change. Demographic variables—seniority and education—indicate a significant relationship with the outcome variable.

The current world is characterized by VUCAT (Volatility, Uncertainty, Complexity, Ambiguity, and Technology). These elements provoke in workers a need for leadership that must be addressed continuously (Deepika & Chitranshi, 2021). Companies must dedicate efforts to leading their employees in a homogeneous way for team integration (Contreras et al., 2020; Sandeep & Latasri, 2021). It should be noted that remote work is a permanent feature for more organizations. Despite the difficulties that workers at all levels have had to adapt to remote work, an adaptation has been achieved in general terms based on communication technology. However, in many cases, it has altered the traditional performance of resources in companies, which makes the leading global and local institutions design new rules in favor of the health of workers and, at the same time, contribute to their better performance. For that, managers in the areas of Human Resources, Finance, and Information Technology (IT) must be able to propose strategies to take care of the human capital of companies in all their departments. For the process to work efficiently, leadership is necessary. The transition of workers from the Asia Pacific region to teleworking, the influence of leadership, and the impact on job satisfaction, which is a measure that shows the optimal management of this new way of working in companies, are described. The organizational models related to business management, the barriers in the first steps of teleworking, the strategies used, the legislation created, and the results achieved by the company are described.

Military and unmanned aerial vehicles (UAV) applications like rocket motor casings, missile covers and ship hulls use components that are made of maraging steel. Maraging steel has properties that are superior to other metals, making it more suitable for the fabrication of such components. A grey relational analysis (GRA) that is based on the Taguchi method has been utilised in the current study to optimise a laser beam welding (LBW) process. Further aspects such as GRA's optimum ranges and percentage contributions were also estimated.

A Taguchi L16 orthogonal array is utilised to design and conduct the experiments. Laser power (LP), welding speed (WS) and focal position (FP) are the three parameters are chosen for the process of welding. The output responses are the upper width of the heat-affected zone (HAZup), the upper width of the fusion zone (FZup) and the depth of penetration (DOP). The effect of the above key parameters on the responses was examined using an analysis of variance (ANOVA).

The results of ANOVA reveal that the parameter that has the most influence on the overall grey relational grade (GRG) is the FP. Finally, metallographic characterisation and a microstructural analysis are conducted on the weld bead geometry to demarcate the zone of HAZ and fusion zone (FZ).

As the most important criteria for LBW of maraging steels is the provision of higher DOP, higher FZ width and lower heat-affected zone, the study intended to prove the applicability of GRA technique in solving multi-objective optimisation problems in applications like defence and unmanned systems.

The purpose of this paper is to present a new method to optimize the electro chemical machining process parameters for titanium alloy (Ti6Al4V).

The desirability function analysis (DFA), fuzzy set theory with trapezoidal membership function and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method are used to optimize the electro chemical machining process parameters for titanium alloy (Ti6Al4V). In recent years, the utilization of titanium and its alloys, especially of Ti6Al4V materials, in many different engineering fields has undergone a tremendous increase. The ECM process has a potential in the machining of Ti6Al4V. The machining parameters such as electrolyte concentration, current, applied voltage and feed rate with multiple responses such as material removal rate (MRR) and surface roughness (SR) are considered. Experimental work is carried out on Ti6Al4V using second order central composite rotatable design. The two responses are converted into global knit quality index using DFA. Fuzzy set theory with trapezoidal membership function is used to convert all machining parameters and responses into fuzzy values. Then a TOPSIS approach which determines the optimal machining parameters in terms of higher closeness coefficient is proposed to optimize the machining parameters of ECM for titanium alloy. Finally, ANOVA is performed to investigate the significance of each machining parameter and to identify the most influencing factor which affects the process responses.

The optimal machining parameters for ECM process are determined using desirability function analysis, fuzzy set theory and TOPSIS.

A new method is proposed to optimize the electro chemical machining process parameters for titanium alloy.

The purpose of this paper is to report a study in which the feasibility of conducting probabilistic finite element analysis (FEA) for crane hook design has been explored.

This paper presents the results of probabilistic analysis, where in the input random variables are varied and corresponding variations in the output parameters were observed. In this study, material properties and load have been considered as random input variables and the maximum stress, maximum deflection variations were considered as output random variables.

The probability of occurrence of output variation and the sensitivity of output variables on the input variables are the important results generated from this analysis. By performing this probabilistic analysis, the random selection of factor of safety could be avoided.

The implementation study has been carried out for a single product.

The usage of the approach will indicate the importance of probabilistic analysis in product design and development process. This process will enable the organization to compete in the global market.

A case study has been reported to indicate the feasibility of performing probabilistic FEA for crane hook design. Hence, the contributions are original.

The article makes a modest attempt to explore the level of financial literacy (FL) amongst the farmers in India. An effort was also made to unearth the factors affecting such FL.

The study used secondary data on 11,030 farmers across various regions of India from the Financial Inclusion Insight Survey, 2017. Standard and Poor Global FL questions were used to measure the level of FL amongst the respondents. In addition to the appropriate statistical tools and techniques, the censored tobit regression model and generalized structural equation model were applied to explore the determinants of FL of the Indian farmers.

The outcome of the study indicated that the majority of Indian farmers are financially illiterate. The average FL score obtained by the sample farmers was found to be only 33%. The results of the study signaled significant regional variation in FL amongst the farmers across India. Apart from the regional variation in FL, farmer type, state-specific agricultural productivity, gender, marital status, age, educational attainment and financial inclusion were found to be the major determinants of the FL amongst the farmers.

Evaluation of FL amongst farmers is scanty in the literature in developed nations and especially in the context of emerging economies, like India. The authors tried to fill this gap by exploring FL and its determinants amongst Indian farmers. In addition to this, the study for the first time used a comprehensive and rich dataset of 11,030 Indian farmers while exploring the level of FL and its determinants.

This paper aims to identify challenges and facilitating factors in interorganizational knowledge acquisition. For this purpose, the interorganizational settings of the Orange Line Metro Train System and Sustainable bus rapid transit (BRT) Corridor in Pakistan are examined.

This study uses an exploratory multiple case study approach. The empirical data encompasses semi-structured interviews and archival documents. Within and cross-case analyses are used for analyzing the data.

The findings identify challenges such as time pressure, knowledge hiding, finding credible information sources, organizational red tape and facilitating factors such as clear objectives, individual interest and personal commitment, and revisiting the organizational culture and environment in which interorganizational knowledge acquisition takes place.

By examining knowledge acquisition in interorganizational projects, this study contributes to the literature on knowledge-based theory.

The purpose of this paper is to simulate the flow of a conducting fluid past a circular cylinder placed centrally in a channel subjected to an imposed transverse magnetic field to study the effect of a magnetic field on vortex shedding at different Reynolds numbers varying from 50 to 250.

The two‐dimensional incompressible laminar viscous flow equations are solved using a second‐order implicit unstructured collocated grid finite volume method.

An imposed transverse magnetic field markedly reduces the unsteady lift amplitude indicating a reduction in the strength of the shed vortices. It is observed that the periodic vortex shedding at the higher Reynolds numbers can be completely suppressed if a sufficiently strong magnetic field is imposed. The required magnetic field strength to suppress shedding increases with Reynolds number. The simulation shows that the separated zone behind the cylinder in a steady flow is reduced as the magnetic field strength is increased.

In this paper, due attention is given to resolve and study the unsteady cylinder wake and its interaction with the shear‐layer on the channel wall in the presence of a magnetic field. A critical value of the Hartmann number for complete suppression of the shedding at a given Reynolds number is found.

This chapter examines three common fintech use cases transforming the financial industry. First, the chapter introduces fintech's role in enhancing financial services and promoting financial inclusion, especially through digital platforms. Second, it investigates various fintech applications that support financial institution management by harnessing the power of artificial intelligence (AI) and machine learning (ML). Finally, the chapter explores fintech use cases related to the regulatory environment, including regulatory technology (regtech), blockchain technology, and cryptocurrencies. The insights presented in this chapter cater to researchers and practitioners keen on better understanding fintech's diverse applications in the ever-evolving financial industry landscape.

This chapter explores the role of artificial intelligence (AI), particularly its subfield of machine learning (ML) methods, as a core technology of the fintech revolution in the financial services industry. It simplifies some of the complex concepts related to AI by introducing the main ML paradigms and related techno-methodic aspects. This chapter uses real-world examples to illustrate how next-generation AI powered by ML is transforming the financial services industry. Next, in illustrating the risks associated with AI adoption, this chapter discusses the need for regulation to address the essential facets of AI governance, including transparency, accountability, ethics, and responsible use. Lastly, it looks at emerging regulatory approaches across leading global jurisdictions. The primary goal is to give readers an initial understanding of AI's profound impact on the financial sector.