Jaypee University of Engineering and Technology

Modern econometric analysis increasingly relies on large-scale administrative, financial and platform datasets in which anomalies such as outliers, structural breaks and contamination are common. At the same time, these datasets are highly sensitive, creating binding privacy constraints that affect what can be computed and disclosed. This chapter develops a unified perspective on robust anomaly detection under privacy preservation. It reviews robust econometric tools including M-estimators and high-breakdown estimators and formal robustness concepts such as influence functions and breakdown points. It then examines privacy-preserving mechanisms, including differential privacy, secure multiparty computation, federated estimation and synthetic data and explains how privacy-induced noise changes bias, variance and inferential reliability. The chapter proposes an operational framework that connects robustness and privacy through sensitivity bounds.

Anomalies in econometric data arise from measurement error, rare shocks and structural change and may distort identification, bias estimation and invalidate standard inference. Because anomaly labels are often incomplete, selectively observed or endogenous, anomaly detection cannot be treated as a purely supervised prediction task. This chapter provides a unified econometric review of supervised, semi-supervised and unsupervised paradigms, covering influence diagnostics, robust regression, outlier tests, one-class methods, isolation-based algorithms and representation learning. The discussion emphasizes robustness to contamination, misspecification and regime instability, connecting classical diagnostics with modern machine learning approaches. Practical guidance is provided on selecting appropriate paradigms, interpreting anomaly scores as screening tools and addressing inference challenges following data-dependent detection.

As higher education settings are becoming more digital and knowledge-intensive, it is important to understand how innovation and engagement behaviours facilitate knowledge creation, sharing and retention. Anchored in Social Exchange Theory (SET) and Self-Determination Theory (SDT), this research proposes a research model to investigate how Entrepreneurial Leadership (EL) influences Innovative Work Behaviour (IWB) and engagement among faculty members in the Indian higher education system. Additionally, this study also examines the mediating role of Employee Engagement (EE) and the moderating effect of Psychological Contract Breach (PCB). Using the data collected from 588 faculty members across public and private HEIs in India, this study applied the Structural Equation Modelling (SEM) approach. The results revealed that EL positively influences both EE and IWB, implying that entrepreneurial leaders encourage faculty to engage more deeply with their work, which leads to higher levels of innovation. EE is found to mediate the relationship partially between EL and IWB, demonstrating that engaged employees are more likely to exhibit innovative behaviours. Additionally, PCB is found to moderate the relationship negatively between EL and EE, which indicates that the positive effects of EL are weakened when faculty members perceive unmet organisational promises. However, PCB does not significantly moderate the direct relationship between EL and IWB, suggesting that strong leadership can promote innovation even amid perceived contract breaches. The study extends KM literature by linking leadership behaviours to faculty-driven knowledge processes and enabling faculty-led innovations to scale through technology-enabled learning platforms. It also provides actionable insights for institutions seeking to foster an innovation-oriented knowledge culture aligned with NEP 2020.

Moral capital represents the accumulated ethical goodwill and legitimacy that organizations cultivate through consistent value-aligned behavior, forming a critical foundation for sustainable value creation. This chapter examines the intricate relationships between moral capital, trust reserves, and social license to operate (SLO), exploring how these interconnected constructs shape organizational resilience and stakeholder relationships in contemporary business environments. Through comprehensive theoretical analysis and empirical insights, we demonstrate that moral capital functions as a strategic asset that enables organizations to navigate complex stakeholder expectations, regulatory landscapes, and social accountability pressures. The chapter develops an integrative framework linking moral capital accumulation to trust-building mechanisms and SLO maintenance, while examining the dynamic processes through which organizations can build, preserve, or deplete these critical resources.

Chatter remains a critical limitation in milling operations, impairing surface integrity, reducing tool life, and limiting productivity. Accurate real-time prediction of chatter is essential for achieving process stability and enhancing machining efficiency. This paper provides a comparative evaluation of Support Vector Machines (SVM) and Artificial Neural Networks (ANN) combined with Variational Mode Decomposition (VMD) for the accurate identification of chatter in real-time vibration data. The milling vibration signals are decomposed into an Intrinsic Mode Functions (IMFs) with VMD to obtain chatter sensitive features. Axial depth of cut, table feed and Spindle speed data combined with these characteristics are used as input to both ANN and SVM models. The ANN model uses Tangent Sigmoid (TANSIG) activation function and six training algorithms, out of which Levenberg-Marquardt (LM) is found to produce the best results. Experimental verification proved that the prediction accuracy of 91.44% was outstripped (over 87.21%) in terms of the SVM model by the ANN model. The robustness of this framework in detecting stable, transitional, and unstable cutting zones has also been demonstrated through Stability Lobe Diagrams (SLDs) analysis. The proposed VMD-ANN approach offers an accurate and efficient solution to chatter prediction and parameter optimisation in real-time, which further contributes to higher material removal rates, better surface quality, and a longer tool life for smart manufacturing applications.