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This study aims towards developing a conceptual framework by systematically reviewing the available literature with reference to job crafting under the lens of an emerging economy from South Asia, i.e. India, which is the largest country and the largest economy in the South Asian region.

The study employs a hybrid methodology of a systematic literature review (SLR) and bibliometric analysis using VOSviewer and Biblioshiny. Bibliometric analysis provides glimpses into the current state of knowledge like-trend of publication, influential authors, collaboration with foreign authors, the major themes and studied topics on job crafting in India etc. Further, a detailed SLR of the selected articles led to the development of the conceptual framework consisting of the enablers and outcomes of job crafting.

It discusses implications for academia, business and society at large, and also provides valuable insights to policymakers and practitioners paving the way for better adoption, customization and implementation of job crafting initiatives.

Owing to its own unique social, cultural, and economic characteristics, the dynamics of job crafting in India may vary from other countries and regions which can also be reflective of how job crafting operates in South Asia in general. As job crafting was conceptualized and later evolved mostly in the western context, our study assumes greater significance as it is the first study which attempts to systematically review the job crafting literature to understand how job crafting manifests in the Indian context and presents a conceptual framework for the same.

This paper investigates how stockist brand equity is created in the unorganized pharma retail sector in emerging markets despite the absence of any proactive brand-building efforts by distribution firms (stockists). In addition, this study also aims to unravel the sources of stockist brand equity.

Drawing from grounded theory, a qualitative research design using semi-structured interviews was conducted, and a theoretical saturation approach was used for this purpose. The retailer–stockist (business-to-business [B2B]) relationship in the Indian pharmaceutical market context was used as the study context.

The findings show that in the absence of any brand-building activities, stockists’ sales representatives play an important role in creating stockist brand awareness. In addition, word of mouth from other retailers also plays a minor role. Rational and emotional brand associations which are strong, favorable and unique are created 1) directly by the services provided by stockists, which includes product availability, deliverability, accuracy in billing and batch numbers, credit facilities and discounts, handling issues such as product expiry, and 2) indirectly by the services provided by stockists’ sales representatives which includes their frequency of visits, proactive customer orientation rather than sales orientation, fulfilling commitments and relationship with retailers. The strength of the retailer–stockist (B2B) relationship also depends on retailer-driven factors and other external factors discussed in this study.

Strong stockist brand equity helps build trust and loyalty with pharmacies, ensuring a consistent and timely supply of medicinal products, which will benefit their end consumers or society.

This study identifies the antecedents determining the strength of the retailer–stockist (B2B) relationship, a precursor for establishing retailer-based stockist brand equity in the unorganized sector. This study also highlights the consequences of establishing strong retailer-based stockist brand equity.

The study delves into the role played by cutting-edge data analytics, machine learning, and innovative technologies in reshaping traditional insurance practices. The primary goal of this review is to juxtapose findings from the literature sources, enabling a comprehensive analysis of the current state of implementation.

Systematic narrative review methodology has been applied to the present study. Scopus database has been used for the manuscripts ranging from year 2020 to 2024 considering the 5-year rule. 74 manuscripts were reviewed to navigate the landscape of data-driven revolution, unlocking the potential to elevate insurance operations to new heights. Two research questions about the impact of data alchemy on operational efficiency and insights and its contribution to reshaping the future landscape of insurance practices have been answered.

This approach captured the interplay between the theoretical potential for insurance and the practical realities of implementation of advanced practices, drawing upon the collective expertise within the field. By doing so, the article discerned the trajectory of the insurance sector concerning the advanced data alchemy observed in the industry.

The current research contributes to the broader area of data alchemy in the insurance industry. The transformative power of big data analytics lies in its capacity to turn vast and diverse datasets into valuable insights, driving innovation, informed decision-making, and improved business outcomes across various sectors. Notably, the research extends the body of literature exploring the impact of data alchemy on operational efficiency and insights, area where limited studies have been conducted.

This article targeted to experimentally examine the impact of several considered process parameters namely, applied voltage (AV), tool feed rate, electrolyte concentration and pulse frequency (PF), on the material removal rate (MRR) and radial overcut (ROC) while performing shaped tube drilling of aviation grade Inconel 625 super alloy through electrochemical machining principle. Further, an attempt has also been made to develop mathematical models for the process responses along with advanced optimization with evolutionary methods.

The central composite rotatable design matrix was used to scheme out the experiments in the present study. The consistency and accuracy of the developed mathematical models were confirmed through statistical results. Additionally, a field emission scanning electron microscope analysis was conducted to assess and analyze the microstructure of the machined work samples. The study also seeks to optimize the selected process inputs for MRR and ROC through the implementation of the desirability method, particle swarm optimization (PSO) and Teaching Learning-Based Optimization (TLBO).

The ROC is significantly influenced by the input parameters, specifically the PF and AV. Less ROC values were observed when the high PF with moderate AV. The minimum and maximum values of ROC and MRR were obtained as; 135.128 µm and 380.720 µm; 1.37 mg/min and 2.3707 mg/min, correspondingly. The best optimized confirmatory results were obtained through the TLBO approach, with an MRR value of 3.1587 mg/min and a ROC of 71.9629 µm, in comparison to the PSO and desirability approaches.

The various challenges associated with the productive machining of aviation grade Inconel 625 superalloy have been explored experimentally. The conducted experimentation has been performed on the in-house fabricated micro-electrochemical setup capable of performing a variety of advanced machining operations at the miniaturized level. Further, the application of shaped tube drilling while processing aviation grade Inconel 625 superalloy has been explored with the developed micro-ECM set-up. Moreover, the performed microstructure analysis of the machined work samples has elaborated and explored the various associated surface integrity aspects which are quite crucial when it comes to real-life aerospace-related applications. The utility of designed experiments has further made the attempted experimental analysis more fruitful and qualitative too.

This study aims to introduce an image-based method to determine the processing window for a given alloy system using laser powder bed fusion equipment based on achieving the desired melting mode across multiple materials for powder-free specimens. The method uses a convolutional neural network trained to classify different track morphologies across different alloy systems to select appropriate printing settings. This method is intended for the development of new alloy systems, where the powder feedstock may be unavailable, or prohibitively expensive to manufacture.

A convolutional neural network is designed from scratch to identify the 4 key melting modes that are observed in laser powder bed fusion additive manufacturing across different alloy systems. To increase the prediction accuracy and generalisation accuracy across different materials, the network is trained using a novel hybrid data set that combines fully unsupervised learning with semi-supervised learning.

This study demonstrates that our convolutional network with a novel hybrid training approach can be generalised across different materials, and k-fold validation shows that the model retains good accuracy with changing training conditions. The model can predict the processing maps for the different alloys with an accuracy of up to 96% in some cases. It is also shown that powder-free single-track experiments are a useful indicator for predicting the final print quality of a component.

The “invariant information clustering” (IIC) approach is applied to process optimisation for additive manufacturing, and a novel hybrid data set construction approach that accounts for uncertainty in the ground truth data, enables the trained convolutional model to perform across a range of different materials and most importantly, generalise to materials outside of the training data set. Compared to the traditional cross-sectioning approach, this method considers the whole length of the single track when determining the melting mode.