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A persistent and increasing pattern in cash holdings was notable in the aggregate behaviour of Indian corporations around the period from 2007–2008 to 2012–2013. Extant literature suggests that agency conflicts and financing frictions are important determinants of cash holdings. In this chapter the author aims to shed light on the role of corporate governance (CG) in the determination of cash holdings and examined how ownership structure, board and audit-related attributes (used as proxies for the nature of CG) impact cash holdings in the context of an emerging economy, like India. The author employed four different measures of cash and liquidity and 24 structural indicators of CG. Using principal component analysis, the author offers an exploratory inquiry into the dimensions of CG. Thereafter, multiple regression was used to delve into the association between cash holdings (the dependent variable) and CG. Using a sample of 58 top-listed companies the results revealed that the quality of firm-level CG is important in deciding corporate cash holdings. The author reported that firms with stronger CG tend to reduce cash balances and have higher capital expenditures, while in firms with entrenched managers having high cash reserves invest more in current assets. Firms also hold cash for financial flexibility and to take advantage of strategic opportunities as they present themselves. Parallel to this point is the fact that larger balances help firms to avoid uncertainty and hedge themselves against the difficulty of accessing external funds.

Supplier selection (SS) is one of the prime competencies in a sourcing decision. Taking into account the key role played by suppliers in facilitating the implementation of green supply chain management (GSCM), it is somewhat surprising that very little research attention has been imparted to the development of a strategic sourcing model for GSCM. This research aims to develop a strategic sourcing framework in which supplier organizations are prioritized and ranked based on their GSCM performance. Accordingly, the benchmark organization is identified and its strategy is explored for GSCM performance improvement.

The research develops an innovative GSCM performance evaluation framework using six parameters, namely, investment in corporate social responsibility, investment in research and development, utilization of renewable energy, total energy consumption, total carbon-di-oxide emissions and total waste generation. An integrated multicriteria decision-making (MCDM) approach is proposed in which the entropy method calculates criteria weights. The Complex Proportional Assessment (COPRAS) and the Grey relational analysis (GRA) methods are used to rank supplier organizations based on their performance scores. A real-world case of green supplier selection (GSS) is considered in which five leading India-based automobile manufacturing organizations (Supplier 1, Supplier 2, Supplier 3, Supplier 4 and Supplier 5) are selected. Surveys with industry experts at the strategic, tactical, and operational levels are carried out to collect relevant data.

The results reveal that total carbon dioxide emission is the most influential parameter, as it gains the highest weight. On the contrary, investment in research and development, and total waste generation have no significant impact on GSCM performance. Results show that Supplier 5 secures the top rank. Hence, it is the benchmark organization.

The proposed methodology offers an easy and comprehensive approach to sourcing decisions in the field of GSCM. The entropy weight-based COPRAS and GRA methods offer an error-free channel of decision-making and can be proficiently used to outrank various industrial sectors based on their GSCM performances. This research is specific to the automobile manufacturing supply chain. Therefore, research outcomes may vary across supply chains with distinct characteristics.

The basic propositions of this research are based on a real-world case. Hence, the research findings are practically feasible. The less significant parameters identified in this study would enable managers to impart more attention to vulnerable areas for improvement. This research may help policymakers identify the influential parameters for effective GSCM implementation. As this research considers all aspects of sustainability, the strategies of the benchmark supplier have a direct impact on organizations' overall sustainability. The study would enable practitioners to make various strategies for GSCM performance improvement and to develop a cleaner production system.

The originality of this research lies in the consideration of both economic, social, environmental and operational aspects of sustainability for assessing the GSCM performance of supplier organizations. Quantitative criteria are considered so that vagueness can be removed from the decision. The use of an integrated grey-based approach for developing a strategic sourcing model is another unique feature of this study.

Social media platforms allow near‐unfettered creation and exchange of user generated content (UGC). Drawing from network science, the purpose of this paper is to examine whether high and low quality UGC differ in their connectivity structures in Wikipedia (which consists of interconnected user generated articles).

Using Featured Articles as a proxy for high quality, a network analysis was undertaken of the revision history of six different language Wikipedias, to offer a network‐centric explanation for the emergence of quality in UGC.

The network structure of interactions between articles and contributors plays an important role in the emergence of quality. Specifically the analysis reveals that high‐quality articles cluster in hubs that span structural holes.

The analysis does not capture the strength of interactions between articles and contributors. The implication of this limitation is that quality is viewed as a binary variable. Extensions to this research will relate strength of interactions to different levels of quality in UGC.

The findings help harness the “wisdom of the crowds” effectively. Organisations should nurture users and articles at the structural hubs from an early stage. This can be done through appropriate design of collaborative knowledge systems and development of organisational policies to empower hubs.

The network centric perspective on quality in UGC and the use of a dynamic modelling tool are novel. The paper is of value to researchers in the area of social computing and to practitioners implementing and maintaining such platforms in organisations.

The purpose of this paper, is to predict the various phases and crystal structure from multi-component alloys. Nowadays, the concept and strategies of the development of multi-principal element alloys (MPEAs) significantly increase the count of the potential candidate of alloy systems, which demand proper screening of large number of alloy systems based on the nature of their phase and structure. Experimentally obtained data linking elemental properties and their resulting phases for MPEAs is profused; hence, there is a strong scope for categorization/classification of MPEAs based on structural features of the resultant phase along with distinctive connections between elemental properties and phases.

In this paper, several machine-learning algorithms have been used to recognize the underlying data pattern using data sets to design MPEAs and classify them based on structural features of their resultant phase such as single-phase solid solution, amorphous and intermetallic compounds. Further classification of MPEAs having single-phase solid solution is performed based on crystal structure using an ensemble-based machine-learning algorithm known as random-forest algorithm.

The model developed by implementing random-forest algorithm has resulted in an accuracy of 91 per cent for phase prediction and 93 per cent for crystal structure prediction for single-phase solid solution class of MPEAs. Five input parameters are used in the prediction model namely, valence electron concentration, difference in the pauling negativeness, atomic size difference, mixing enthalpy and mixing entropy. It has been found that the valence electron concentration is the most important feature with respect to prediction of phases. To avoid overfitting problem, fivefold cross-validation has been performed. To understand the comparative performance, different algorithms such as K-nearest Neighbor, support vector machine, logistic regression, naïve-based approach, decision tree and neural network have been used in the data set.

In this paper, the authors described the phase selection and crystal structure prediction mechanism in MPEA data set and have achieved better accuracy using machine learning.

The present paper deals with the people and society of a state in India which was known as “the hidden land”. Since Independence, this predominantly tribal society has been in a phase of transition from near isolation to the assimilation of the market economy, giving rise to certain environmental problems. The paper attempts to analyse the linkage between environment and sustainable development in the hilly regions of North East India by considering the case of indigenous shifting cultivation (jhum) techniques practised on a large scale in Arunachal Pradesh. The author feels that a sustainable hill area development requires the blending of traditional and modern techniques and the revival of old tribal beliefs and knowledge regarding the preservation of environment.

The purpose of this paper is to study the transient waves caused by a line heat source with a stable internal heat source inside isotropic homogenous thermoelastic perfectly conducting half‐space permeate into a uniform magnetic field. The formulation is applied under three theories of generalized thermoelasticity Lord‐Shulman (L‐S) theory with one relaxation time, Green‐Lindsay (G‐L) theory with two relaxation times, as well as the classical dynamical coupled theory. The problem is reduced to the solution of three differential equations by introducing the elastic and thermoelastic potentials.

The normal mode analysis is used to obtain the expressions. Numerical results are given and illustrated graphically. Comparisons are made with the results predicted by the three theories in the presence and absence of magnetic field and the internal heat source.

The results are graphically described for the medium of copper. We can conclude that the magnetic field has a great effect on the displacement components and this effect produces the same trend under the three theories. The results show that the relaxation times have salient effect to the distribution of displacement at small values of time.

The present theoretical results may provide interesting information for experimental scientists /researchers/seismologist working on this subject.

This chapter challenges the urban bias in studies on the middle class in India and underlines the need to focus on the significant role rural middle class plays in economic diversification. Given that more than 23% of the upper middle class are located in rural India, it is surprising to note that their contribution in supporting experience economy remains under-researched. Thus, this chapter fills a key gap in existing studies on rural tourism in India by underlining how rural middle-class has triggered a huge demand for travel within the country itself for rural cultural programmes and different schemes to promote rural heritage sights.