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Mergers and acquisitions (M&A) are common strategies of firms to increase its performance. Although the motives of M&A are different, the determinants are discreet. The purpose of this paper is to determine the factors affecting M&A activities in the Indian pharmaceutical sector.

Using a balanced panel data of the pharmaceuticals sector in India, this study arrives at the determinants of M&A. The authors use regression techniques such as panel probit models, ordered probit models and matching techniques for a comparable and robust estimates for the factors related to M&A activities at firm level.

The empirical findings suggest that export intensity, import intensity, firm size and R&D intensity as the major determinants of M&A in the Indian pharmaceutical sector. In the context of acquisition, there is a riskiness associated with the any business strategy, for to which a firm may choose to finance the deal either via cash, stock or assets. This study further looks at the firm’s decision on the types of acquisitions and arrives at the determinants of such decisions. The factors such as capital intensity were found more important when acquisition by share was undertaken compared to others. The success of the M&A is observed by considering the financial performance of the firm measured in terms of the profit margin at firm level. Using the propensity score matching technique, this study concludes that M&A have a positive effect on the profit margin in the post-M&A scenario.

The study seeks to add to the existing literature and empirical work done in the field of M&A, by not only looking at determinants that motivate a firm to merge and/or acquire but also if the decision of the firm is reflected positively in the firm’s performance. The study concludes that export intensity is an important factor for the pharmaceutical firms in India since exports are a major proportion of firm’s total sales. Technology transfers as technology imports and R&D conducted by the firm are both very important for the drug manufacturers. Any gap in the flow of drugs to the market would hamper the growth of the firm. The growth of the pharmaceutical industry depends on the close collaboration between small R&D units that lack resources to sell their work and large business houses, both domestic and foreign affiliated that will provide them the required market. However, all comprehensive work has been done in this study to cover all aspects of M&A including the financial pathway taken by the firm yet, the study has certain limitations as it does not take into account the entry and exit of firms from the industry.

Policies related to small and large firms should aim at making them export oriented and multinational affiliated to compete at the international market with corporate restructuring through M&A. This will help in firm’s growth and sharing of knowledge capital.

No such study is conducted that differentiate type of M&A and their determinants for the Indian pharma sector.

Supply chain performance (SCP) extent can be attributed as a function of multiple criteria/attributes. Most of the criterions/attributes being intangible in nature; SCP appraisement relies on the subjective judgment of the decision makers. Moreover, quantitative appraisement of SCP appears to be very difficult due to involvement of ill-defined (vague) performance measures as well as metrics. The purpose of this paper is to develop an efficient decision support system (DSS) to facilitate SCP appraisement, benchmarking and related decision making.

This study explores the concept of fuzzy logic in order to tackle incomplete and inconsistent subjective judgment of the decision makers’ whilst evaluating supply chain’s overall performance. Grey relational analysis has been adopted in the later stage to derive appropriate ranking of alternative companies/enterprises (in the same industry) in view of ongoing SCP extent.

In this work, a performance appraisement index system has been postulated to gather evaluation information (weights and ratings) in relation to SCP measures and metrics. Combining the concepts of fuzzy set theory, entropy, ideal and grey relation analysis, a fuzzy grey relation method for SCP benchmarking problem has been presented. First, triangular fuzzy numbers and linguistic evaluation information characterized by triangular fuzzy numbers have been used to evaluate the importance weights of all criteria and the superiority of all alternatives vs various criteria above the alternative level. Then, the concept of entropy has been utilized to solve the adjusted integration weight of all objective criteria above the alternative level. Moreover, using the concept of the grey ration grades, various alternatives have been ranked accordingly.

Finally, an empirical example of selecting most appropriate company has been used to demonstrate the ease of applicability of the aforesaid approach. The study results showed that this method appears to be an effective means for tackling multi-criteria decision-making problems in uncertain environments. Empirical data have been analysed and results obtained thereof, have been reported to exhibit application potential of the said fuzzy grey relation based DSS in appropriate situation.

With respect to the multiple-attribute decision-making problem with subjective preference for a certain attribute whose weight-value range have been given over other attributes whose weight values are unknown, a method based on the mean value of the grey number is proposed to analyse the decision-making problem. This method is used to choose a supply-chain partner under the condition that the decision makers have a preference for a certain attribute of various alternatives. The paper aims to discuss these issues.

First, the middle value of the preferred attribute’s weight-value range is supposed to be its weight value according to the content of the mean value of the grey number. Second, to reflect the decision maker’s subjective preference information, an improved optimisation model that requests the minimum deviation between the actual and expected numerical value of each attribute is constructed to assess the attributes’ weights. Third, the correlated degree and the correlation matrix, which are determined by the weight values of all attributes, are used to rank all the alternatives.

This paper provides a method for making a decision when decision makers have a preference for a certain attribute from an array of various alternatives, and the range of the certain attribute’s weight value is given but the weight value of the other attributes is unknown. When applied to supply-chain partner selection, this method proves feasible and effective.

This method is feasible and effective when applied to supply-chain partner selection, and can be applied to other kinds of decision-making problems. This means it has significant theoretical importance and extensive practical value.

Based on the mean value of the grey number, an optimisation model is built to determine the importance degree of each attribute, then the correlated degree of each alternative is combined to rank all the alternatives. This method can suit the decision makers’ subjective preference for a certain attribute well.

The purpose of this study is to investigate the best fleet for a new purchase based on multi-objective optimization on the basis of ratio (MOORA), reference point and multi-MOORA methods. This study further identifies critical parameters for fleet performance monitoring and exploring optimum range of critical parameters using Monte Carlo simulation. At the end of this study, fleet maintenance management and operations have been discussed in the perspectives of risk management.

Fleet categories and fleet performance monitoring parameters have been identified using the literature survey and Delphi method. Further, real-time data has been analyzed using MOORA, reference point and multi-MOORA methods. Taguchi and full factorial design of experiment (DOE) are used to investigate critical parameters for fleet performance monitoring.

Fleet performance monitoring is done based on fuel consumption (FC), CO₂ emission (CE), coolant temperature (CT), fleet rating, revenue generation (RG), fleet utilization, total weight and ambient temperature. MOORA, reference point and multi-MOORA methods suggested the common best alternative for a particular category of the fleet (compact, hatchback and sedan). FC and RG are the critical parameters for monitoring the fleet performance.

The geographical aspects have not been considered for this study.

A pilot run of 300 fleets shows saving of Rs. 2,611,013/- (US$36,264.065), which comprises total maintenance cost [Rs. 1,749,033/- (US$24,292.125)] and FC cost [Rs. 861,980/- (US$11,971.94)] annually.

Reduction in CE (4.83%) creates a positive impact on human health. The reduction in the breakdown maintenance of fleet improves the reliability of fleet services.

This study investigates the most useful parameters for fleet management are FC, CE, CT. Taguchi DOE and full factorial DOE have identified FC and RG as a most critical parameters for fleet health/performance monitoring.

The objective of this paper is to find out the rationale for institutional repository (IR) categories, and the challenges in sustainable development of open scholarship to facilitate scholarly communication.

This is a critical study method adopted to find out the categories of IR and various developmental challenges with the aid of related literature.

IR categories and their nomenclature are found to be overlapping when used by repository registries and librarians alike. As the digital objects in the repositories are expanding, with a requirement to store new entities as diverse as linked data and web archives, IR categorization becomes difficult to determine. Even though the growth of IRs in India is phenomenal, concerted research and development efforts to strengthen digital infrastructure and repository solutions in multilingual settings are slow.

With the rapid growth of IRs worldwide, the categories with which these are classified is examined. As a contentious player in scholarly communication process, the challenges of IRs' development are underlined. The prospects and deployment of IRs requires funding and an appropriately skilled workforce, along with government support. This is viewed from global and Indian perspectives.

One of the sources for the increase in the carbon footprint on the earth is the manufacturing of cement, which causes a severer environmental impact. Abundant research is going on to diminish CO₂ content in the atmosphere by appropriate utilization of waste by-products of industries. Alkali-activated slag concrete (AASC) is an innovative green new concrete made by complete replacement of cement various supplementary cementitious raw materials. Concrete is a versatile material used in different fields of structures, so it is very important to study the durability in different exposures along with the strength. The purpose of this paper is to study the performance of AASC by incorporating quartz sand as fine aggregate under different exposure conditions.

The materials for this innovative AASC are selected based on preliminary studies and literature surveys. Based on numerous trials a better performance mix proportion of AASC with quartz sand is developed with 1:2:4 mix proportion, 0.8 alkali Binder ratio, 19 M of NaOH and 50% concentration of Na₂SiO₃. Subsequently, AASC cubes are prepared and exposed for 3, 7, 14, 28, 56, 90, 112, 180, 252 and 365 days in ambient, acid, alkaline, sulfate, chloride and seawater and tested for compressive strength. In addition, to study the microstructural characteristics, scanning electron microscope (SEM), energy dispersive X-ray analysis and X-ray diffraction analysis was also performed.

Long-term performance of AASC developed with quartz sand is very good in the ambient, alkaline environment of 5% NaOH and seawater with the highest compressive strength values of 51.8, 50.83 and 64.46, respectively. A decrease in compressive strengths was observed after the age of 14, 56 and 112 days for acid, chloride and sulfate exposure conditions, respectively. SEM image shows a denser microstructure of AASC matrix for ambient, alkaline of 5% NaOH and seawater.

The proposed AASC is prepared with a mix proportion of 1:2:4, so the other proportions of AASC need to verify. In general plain, AASC is not used in practice except in few applications, in this work the effect of reinforced AASC is not checked. The real environmental exposure in fields may not create for AASC, as it was tested in different exposure conditions in the laboratory.

The developed AASC is recommended in practical applications where early strength is required, where the climate is hot, where water is scarce for curing, offshore and onshore constructions exposed to the marine environment and alkaline environment industries like breweries, distilleries and sewage treatment plants. As AASC is recommended for ambient air and in other exposures, its implementation as a construction material will reduce the carbon footprint.

The developed AASC mix proportion 1:2:4 is an economical mix, because of low binder content, but it exhibits a higher early age compressive strength value of 45.6 MPa at the age of 3 days. The compressive strength increases linearly with age from 3 to 365 days when exposed to seawater and ambient air. The performance of AASC is very good in the ambient, alkaline environment and seawater compared to other exposure conditions.

The purpose of this paper is to investigate the role of personality factors in influencing mentoring relationships in the South-Asian context.

The sample included 363 subjects from public and private sector organizations in North India.

Results revealed that in the Indian context, conscientiousness acts as significant predictor of perceived psychosocial mentoring, agreeableness acts as significant predictor of perceived career mentoring support, and emotional stability acts as significant predictor of both categories of mentoring relationships. Further, managers employed in public sector organizations were found to be high on all the Big Five personality factors and mentoring functions in contrast to managers from private sector organizations.

Overall, the results suggest that mentoring relationships should operate in organizations with a firm understanding of employees’ personality traits. Implications and future research directions were also discussed. Further, suggestions have also been given for incorporating various interventions in order to handle employees with different personality attributes such as counseling for helping emotionally unstable employees manage their emotions and stress.

To the knowledge, this is the first study that seeks to examine impact of personality factors on mentoring relationships in the South-Asian context.

The metaverse is a virtual world where users can communicate with each other in a computer-generated environment. The use of metaverse technology has the potential to revolutionize the way businesses operate, interact with customers, and collaborate with employees. However, several obstacles must be addressed and overcome to ensure the successful implementation of metaverse technology. This study aims to examine the implementation of metaverse technology in the management of an organization's supply chain, with a focus on predicting potential barriers to provide suitable strategies.

Covariance-based structural equation modeling (CB-SEM) was used to test the model. In addition, artificial neural network modeling (ANN) was also performed.

The CB-SEM results revealed that a firm's technological limitations are among the most significant barriers to implementing metaverse technology in the supply chain management (SCM). The ANN results further highlighted that the firm's technological limitations are the most crucial input factors, followed by a lack of governance and standardization, integration challenges, poor diffusion through the network, traditional organizational culture, lack of stakeholder commitment, lack of collaboration and low perception of value by customers.

Because metaverse technology has the potential to provide organizations with a competitive advantage, increase productivity, improve customer experience and stimulate creativity, it is crucial to discuss and develop solutions to implementation challenges in the business world. Companies can position themselves for success in this fascinating and quickly changing technological landscape by conquering these challenges.

This study provides insights to metaverse technology developers and supply chain practitioners for successful implementation in SCM, as well as theoretical contributions for supply chain managers aiming to implement such environments.

Now days, various techniques are used for controlling the plants. New ideas are evolving day by day to get better-quality control for various industrial processes to produce high-quality products. Currently, the focus of this research is being emphasized on application of nature-inspired algorithms in control systems. The purpose of this paper is to apply a nature-inspired algorithm called Ant Lion Optimizer (ALO) for the design of proportional-integrator-derivative (PID) controller for an automatic voltage regulator (AVR) system.

For the design of the PID controller, the ALO algorithm is considered as a designing tool for obtaining the optimal values of the controller parameter. All the simulations are carried out in Simulink/MATLAB environment. A comparative study is carried out with some modern nature-inspired algorithm to describe the advantages of this tuning method.

The proposed method has superiority value in transient and frequency domain analysis than the other published heuristic optimization algorithms. The presented approach has almost no variation in transient response when varying time constants of the system parameter, such as exciter, generator, amplifier and sensor from −50 per cent to +50 per cent. In addition, the close loop system is robust against any disturbances such as input–output disturbances and parametric uncertainty, as the sensitivity values are nearly equal to one.

The proposed method presents the design and performance analysis of proportional integral derivate (PID) controller for an AVR system using the recently proposed ALO.

Development project implementation involving ecological and social externalities is an iterative process due to dynamic challenges emerging from the need to reconcile multiple, and often conflicting, interests of stakeholders. Stakeholder interactions result in learnings for project developers as well as policy makers. This paper aims to track corporate-civil society organizations-state interactions and learnings in case of a port development project in an eco-sensitive and socially challenging region of India.

The authors try to unpack the interactions among various stakeholders in the case of development of Dhamra Port being developed by the Dhamra Port Company Ltd, in the state of Orissa, India.

The authors find that sustainable development policy formulation and implementation is an evolutionary process involving plugging of governance gaps at each successive stage with civil society actors playing an active role.

The study is based on single case of a major port development in India.

The paper highlights that development in emerging economies through large projects is unavoidable. The development-sustainability dilemma cannot be overlooked any further; rather, it needs to be resolved with complete participation of all involved stakeholders.

The paper provides account of long and complex interactions among various players involved in developmental project in an emerging economy. The learnings from the paper will be relevant for policymakers, corporate executives and civil society activists.