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In this paper, using the antecedents, decisions and outcomes (ADO) framework, the factors/key performance indicators (KPIs) most relevant for creating or building a learning organization (LO) are identified. This study aims to contribute to the field of knowledge management (KM) in terms of introducing KPIs to foster a business organization with a continuous learning process, mechanisms of knowledge creation and memorization.

In total, 57 papers were selected for this systematic literature review (SLR) from Web of Science and Scopus covering the period 1985–2019.

The 12 most relevant KPIs are identified based on the literature survey conducted in the field of LO.

The managerial implications of this review paper will be an added advantage to the modern business organization worldwide that have adopted KM practices to foster knowledge management with information technology (IT) infrastructure. As IT infrastructure focuses on knowledge acquisition, dissemination and storage but the KPIs revealed through this review will help in transforming stored information as learning for the organization to improve its overall performance.

This review synthesizes prior studies and provides directions for future research.

With the rise of social media platforms, an increasing number of cases of cyberbullying has reemerged. Every day, large number of people, especially teenagers, become the victim of cyber abuse. A cyberbullied person can have a long-lasting impact on his mind. Due to it, the victim may develop social anxiety, engage in self-harm, go into depression or in the extreme cases, it may lead to suicide. This paper aims to evaluate various techniques to automatically detect cyberbullying from tweets by using machine learning and deep learning approaches.

The authors applied machine learning algorithms approach and after analyzing the experimental results, the authors postulated that deep learning algorithms perform better for the task. Word-embedding techniques were used for word representation for our model training. Pre-trained embedding GloVe was used to generate word embedding. Different versions of GloVe were used and their performance was compared. Bi-directional long short-term memory (BLSTM) was used for classification.

The dataset contains 35,787 labeled tweets. The GloVe840 word embedding technique along with BLSTM provided the best results on the dataset with an accuracy, precision and F1 measure of 92.60%, 96.60% and 94.20%, respectively.

If a word is not present in pre-trained embedding (GloVe), it may be given a random vector representation that may not correspond to the actual meaning of the word. It means that if a word is out of vocabulary (OOV) then it may not be represented suitably which can affect the detection of cyberbullying tweets. The problem may be rectified through the use of character level embedding of words.

The findings of the work may inspire entrepreneurs to leverage the proposed approach to build deployable systems to detect cyberbullying in different contexts such as workplace, school, etc and may also draw the attention of lawmakers and policymakers to create systemic tools to tackle the ills of cyberbullying.

Cyberbullying, if effectively detected may save the victims from various psychological problems which, in turn, may lead society to a healthier and more productive life.

The proposed method produced results that outperform the state-of-the-art approaches in detecting cyberbullying from tweets. It uses a large dataset, created by intelligently merging two publicly available datasets. Further, a comprehensive evaluation of the proposed methodology has been presented.

To estimate the volatility of exchange and stock markets and examine its spillover within and across the member countries of BRICS during COVID-19 and the conflict between Russia and Ukraine.

The study utilizes the “dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH)” approach of Gabauer (2020). The volatility of the markets is calculated following the approach of Parkinson (1980). The sample dataset comprises the daily volatility of the stock and exchange markets for 35 months, from November 2019 to September 2022.

The study confirms the existence of contagion effects among member countries. Volatility spillover between exchange and stock markets is low within the country but substantial across borders. Russian contribution increased significantly during the conflict with Ukraine, and other countries also witnessed a surge in the spillover index during the pandemic and war.

It adds to the body of literature by emphasizing the necessity of comprehending the economies' behavior and interdependence. Offers insightful information to decision-makers who must be more watchful regarding the financial crisis and its regional spillover.

The study is the first to explore the contagion of volatility among the BRICS countries during the two biggest crisis periods of the decade.

This paper aims to develop a computational approach to analyze the mechanical behavior, perfusion bioreactor test and degradation of the designed scaffolds. Five types of pore architecture scaffolds have been made using a computer-aided designed tool and fabricated through fused deposition modeling.

Compressive structural analysis has been performed using the finite element method to forecast the mechanical performance of the scaffolds. Also, the experimental study was done to validate the simulation outcomes. A computational fluid dynamic analysis was performed to ascertain the fluid pressure distribution, velocity profile, wall shear stress, strain rate and permeability of scaffolds. The interconnected pore architecture of the scaffolds plays a crucial role in enhancing the mechanical properties and fluid flow characteristics.

The scaffolds with continuous vertical support columns resulted in better strength because they provide better ways to transfer the load. The pore architecture of the scaffold plays a significant role in the path of fluid flow. Scaffolds with regular interconnected pore architecture showed better accessibility of the fluid. The degradation analysis showed that the degradation rate is dependent on the architecture of the scaffolds because of different surface area to volume ratios.

The simulation results provide a straightforward prediction of the scaffold suitability in terms of mechanical strength, perfusion and degradation behavior.

To investigate the role of domestic and foreign economic policy uncertainty (EPU) in driving the corporate bond yields in emerging markets.

The study utilizes monthly data from January 2008 to June 2023 from the selected emerging economies. The data analysis is conducted using univariate, bivariate and multivariate statistical techniques. The study includes bond market liquidity and global volatility (VIX) as control variables.

Domestic EPU has a significant role in driving corporate bond yields in these markets. The study finds weak evidence to support the role of the USA EPU in influencing corporate bond yields in emerging economies. Domestic EPU holds more weight and influence than the EPU originating from the United States of America.

The findings provide useful insights to policymakers about the potential impact of policy uncertainty on corporate bond yields and enable them to make informed decisions regarding economic policies that maintains financial stability. Understanding the relationship between EPU and corporate bond yields enables investors to optimize their investment decisions in emerging market economies, opens the scope for further research on the interaction between EPU and volatility and other attributes of fixed income markets.

Focuses specifically on the emerging market economies in Asia, providing an in-depth analysis of the dynamics and challenges faced by these countries, Explores the influence of both domestic and the USA EPU on corporate bond yields in emerging markets, offering valuable insights into the transmission channels and impact of EPU from various sources.

The purpose of this study is to investigate the deposition of SS–Al transitional wall using the wire arc directed energy deposition (WA-DED) process with a Cu interlayer. This study also aims to analyse the metallographic properties of the SS–Cu and Al–Cu interfaces and their mechanical properties.

The study used transitional deposition of SS–Al material over each other by incorporating Cu as interlayer between the two. The scanning electron microscope analysis, energy dispersive X-ray analysis, X-ray diffractometer analysis, tensile testing and micro-hardness measurement were performed to investigate the interface characteristics and mechanical properties of the SS–Al transitional wall.

The study discovered that the WA-DED process with a Cu interlayer worked well for the deposition of SS–Al transitional walls. The formation of solid solutions of Fe–Cu and Fe–Si was observed at the SS–Cu interface rather than intermetallic compounds (IMCs), according to the metallographic analysis. On the other hand, three different IMCs were formed at the Al–Cu interface, namely, Al–Cu, Al₂Cu and Al₄Cu₉. The study also observed the formation of a lamellar structure of Al and Al₂Cu at the hypereutectic phase. The mechanical testing revealed that the Al–Cu interface failed without significant deformation, i.e. < 4.73%, indicating the brittleness of the interface.

The study identified the formation of HCP–Fe at the SS–Cu interface, which has not been previously reported in additive manufacturing literature. Furthermore, the study observed the formation of a lamellar structure of Al and Al2Cu phase at the hypereutectic phase, which has not been previously reported in SS–Al transitional wall deposition.

The purpose of this research paper is to evaluate the effects of customer relationship management (CRM) practices on the relationship of organizational learning (OL) with customer satisfaction (CS) essentially dealing with industrial and business organizations. Industrial organizations could benefit from this study’s contributions, where the effectiveness of OL and its antecedents is measured over the level of CS in the presence of CRM practices.

In investigating the dimension of the relationship between OL and CS, the research paper uses the moderating effect of CRM practices to measure the intensity of the relationship between OL and CS. Thirty-nine European business organizations with an Indian presence were selected and a questionnaire survey was conducted on 308 senior employees and managers to collect the relevant data. Hierarchical regression analysis was used to see the interaction effects of the variables and to test the hypothesis.

The research findings reveal that the moderating effect of CRM practices on the relationship between OL and CS is negatively significant and has an effect on increasing or decreasing the level of CS with attempts at OL. Organization size as a moderator between OL and CS was found not significant. This means that the size of an organization cannot affect the direct relationship between OL and CS.

The study is based on a cross-sectional research design which could limit the generalization after a certain period. This study has tried to remove common method bias by avoiding continuity when taking responses for independent, dependent and moderating variables.

OL far beyond its conceptual understanding to practical application is relevant to many fields of studies including human resources development, business and marketing, service marketing, education and so on. The study provides a significant contribution in business and industrial marketing areas in making OL and its shared interpretation effective to achieve a greater degree of CS and value creation.

OL, when taken beyond its conceptual understanding to practical application, is relevant to many fields of study including human resources development, business and marketing, service marketing and education. This study provides a significant contribution to business and industrial marketing areas by making OL and its shared interpretation effective in achieving a greater degree of CS and value creation.

This study is original and unique in establishing the moderating relationship of CRM practices on OL and CS among European business organizations having operations in the Indian business sphere.

Additive manufacturing (AM) or solid freeform fabrication (SFF) technique is extensively used to produce intrinsic 3D structures with high accuracy. Its significant contributions in the field of tissue engineering (TE) have significantly increased in the recent years. TE is used to regenerate or repair impaired tissues which are caused by trauma, disease and injury in human body. There are a number of novel materials such as polymers, ceramics and composites, which possess immense potential for production of scaffolds. However, the major challenge is in developing those bioactive and patient-specific scaffolds, which have a required controlled design like pore architecture with good interconnectivity, optimized porosity and microstructure. Such design not only supports cell proliferation but also promotes good adhesion and differentiation. However, the traditional techniques fail to fulfill all the required specific properties in tissue scaffold. The purpose of this study is to report the review on AM techniques for the fabrication of TE scaffolds.

The present review paper provides a detailed analysis of the widely used AM techniques to construct tissue scaffolds using stereolithography (SLA), selective laser sintering (SLS), fused deposition modeling (FDM), binder jetting (BJ) and advanced or hybrid additive manufacturing methods.

Subsequently, this study also focuses on understanding the concepts of TE scaffolds and their characteristics, working principle of scaffolds fabrication process. Besides this, mechanical properties, characteristics of microstructure, in vitro and in vivo analysis of the fabricated scaffolds have also been discussed in detail.

The review paper highlights the way forward in the area of additive manufacturing applications in TE field by following a systematic review methodology.

The purpose of this paper is to identify the significant corporate social responsibility (CSR) issues and to analyze the interaction among them in order to improve supply chain performance (SCP) system of an organization.

To achieve the objective, a preference rating approach (synthesized in company preference rating (CPR) and company satisfaction assessment (CSA) approaches) has been used in two stages as: stage 1, relative importance rating (RIR) of identified issues has been computed using CPR approach. For this, preference graphs have been developed based on the field expert’s opinions using graph-theory-based representation technique. In stage 2: competitive priority ratings of CSR issues with respect to competitive strategies have been calculated using CSA approach.

Based on RIRs (stage 1), it has been noticed that issue namely “social benefits for employees” is more desirable among all considered issues. In stage 2, based on competitive priority ratings, the issue “social benefits for employees” is more important for an organization to build a better brand reputation through CSR activities and issue “degree of CSR implementation” is more significant in order to improve the SCP of an organization. The issue “environmental protection” helps the organization to reduce their overall environmental impact and also provides an aid in achieving long-term sustainable goals of an organization.

The findings of present research gives an idea about the importance of CSR issues, based on this, managers can decide, which issue is more significant and can help in taking decisions and framing strategies in context of CSR for improving the SCP of their organization effectively and efficiently.

As per the best knowledge, recognized CSR issues have been considered first time to quantify the CSR-based SCP of Indian automobile industries located near around Delhi region, India. This paper may provide an aid to the managers in making the CSR-based policies to enhance their SCP.

The purpose of this paper is to develop a fuzzy methodology approaches based framework for carrying the risk analysis of a real industrial system of a urea fertilizer industry located in northern part of India.

Petri Net approach was applied for representing the series-parallel arrangement of the considered system. Various failure causes related to different subsystems or equipment of the considered system were listed under FMEA approach and their Risk Priority Number was tabulated. Further, to overcome the drawbacks of traditional FMEA approach in risk ranking fuzzy FMEA and grey relation analysis (GRA) approaches were applied within traditional FMEA approach and the ranking results were compared for better and effective decision making of risky components.

The proposed framework has overcome the drawbacks of tradition FMEA approach in an effective and efficient manner. Causes AC7, CL3, ST2, DR3 and NR3 of centrifugal compressor, hot heat exchanger, ammonia convertor reactor, cold condenser and ammonia separator have been identified as the most critical failure causes of the considered system.

The proposed framework has been tested with its application on an ammonia synthesis system of the considered process industry. The risk ranking results would be highly useful in developing a planned maintenance policy for the considered system which further results in improving the system availability.