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Change in culture brought about by effective leadership is at the core of this case. Therefore, two broad topics can be discussed using this case: organizational culture change and Change Leadership OR Role of leaders in organzational change.

The case was prepared using primary data collected through a series of interviews conducted with participants of the change process. The participants included R. Sivanesan, Senior Vice President (Quality, Sourcing and Supply Chain) of Ashok Leyland, many members of the quality team, production department, HR executives and members of the marketing team. Secondary data in the form of an interview of Mr Vinod Dasari published in a popular magazine Autocar Professionals and organizational documents/presentations used during the change process were also used to build the case.

In 2011, when Vinod Dasari took over as the Managing Director and CEO of Ashok Leyland (AL), he hired R. Sivanesan. The quality standards of the vehicles produced in the AL plants in 2011 was far from satisfactory. He decided to change this. Part A of the case discusses the challenges faced by Sivanesan and Vinod Dasari in bringing about a change in the quality management practices at AL. Part B discusses the steps they actually took and the change that resulted from it.

At the end of the case discussion, the participants will be able to develop an understanding of the various aspects of organizational culture and how it manifests itself; become aware of the underlying causes of resistance to change; critically evaluate and apply various theories of change management; create an action plan for changing the culture of any organization; and appreciate the role of leaders as change agents.

The central theme in this case is managing culture change within organizations through effective leadership. Instructors teaching courses in organizational theory, organization structure/culture and leadership will find this case relevant. It is primarily intended for use in MBA and Executive Education programs in Management.

In this paper, the authors propose and empirically test an integrated model which investigates the relationship between POS-E (perceived organizational support for the environment) and employee outcomes, which are employee eco-initiatives (the first category of OCBE), employee psychological capital and alienation. Meaningful work as a mediator between POS-E and employee outcomes was also investigated.

The study utilized a survey method to empirically test the hypothesized relationships on a sample of 303 respondents. For testing, Confirmatory factor analysis for the proposed and alternative models, Structural Equation Modeling (SEM) based on software AMOS, version 20.0 was used. This was to ensure validity and construct distinctiveness among the variables in the study and to evaluate the fit of the hypothesized measurement model in comparison to several alternate models. To estimate the effects of meaningful work (as a mediator) on the association between POS-E and eco-initiatives, psychological capital and alienation, the authors administered Sobel test.

The present research augments the contemporary research on environmental sustainability and employee outcomes by further developing the emerging constructs of perceived organizational support of the environment (POS-E) and organized citizenship behavior toward the environment (OCBE), which is measured by eco-initiatives. The results imply that POS-E is positively associated with eco-initiatives and employee psychological capital and is negatively associated with alienation. The findings further suggest that meaningful work mediates the association between POS-E and all the outcome variables which are: employee-eco-initiatives, psychological capital and alienation.

The findings confirm the desired direction of research and accomplished the research objective of the study. As the consequences of POS-E imply immense value for all stakeholders, decision-makers must also reflect on the means of enhancing employees' understanding. Further, it is imperative, that the organization supports their environmental goals and values, and their green engagement.

Results of the present study exhibit wide practical inferences for the managers. HR managers need to organize the passion for green behavior and work on intrinsic drivers of employee green engagement to let it sustain over a period of time. As society gradually expects increased organizational contributions towards environmental sustainability, this paper indicates that those employees who get an opportunity to act in coordination with environmental objectives will engage in eco-initiatives, exhibit higher psychological capital, and be less likely to feel alienated. The results imply that leaders should examine a diversity of probable interventions to enhance POS-E in order to gain from the initial rise in perceived meaningful work, employee eco-initiatives, increased psychological capital and reduced alienation. These interventions may lead to higher passion for sustainability and green behavior.

Further, this work supports the work of Toffel and Schendler (2013), whose study states that organizations should market their environment and climate initiatives, climate activism, such that customers and suppliers appreciate their leadership, and understands what matters. This work supports the work of Turaga et al. (2010), whose study states that for pro-environment behavior, environment passion is an intrinsic behavior which is needed (see Afsar et al., 2016). The current study enhances the need to trigger employee's sense of pro-environment passion at work place for significant results.

This is a pioneer study, in India which confirms and extends the construct of POS-E using Social Exchange theory as an underpinning theory. We found that POS-E was linked with previously untested employee consequences, like employee eco-initiatives and psychological capital and that it was negatively associated with alienation. Our study confirmed mediator variable to be meaningful work in the relationship between POS-E and psychological capital, alienation and eco-initiatives

This study aims to present a novel system for detection and prevention of black hole and wormhole attacks in wireless sensor network (WSN) based on deep learning model. Here, different phases are included such as assigning the nodes, data collection, detecting black hole and wormhole attacks and preventing black hole and wormhole attacks by optimal path communication. Initially, a set of nodes is assumed for carrying out the communication in WSN. Further, the black hole attacks are detected by the Bait process, and wormhole attacks are detected by the round trip time (RTT) validation process. The data collection procedure is done with the Bait and RTT validation process with attribute information. The gathered data attributes are given for the training in which long short-term memory (LSTM) is used that includes the attack details. This is used for attack detection process. Once they are detected, those attacks are removed from the network using the optimal path selection process. Here, the optimal shortest path is determined by the improvement in the whale optimization algorithm (WOA) that is called as fitness rate-based whale optimization algorithm (FR-WOA). This shortest path communication is carried out based on the multi-objective function using energy, distance, delay and packet delivery ratio as constraints.

This paper implements a detection and prevention of attacks model based on FR-WOA algorithm for the prevention of attacks in the WSNs. With this, this paper aims to accomplish the desired optimization of multi-objective functions.

From the analysis, it is found that the accuracy of the optimized LSTM is better than conventional LSTM. The energy consumption of the proposed FR-WOA with 35 nodes is 7.14% superior to WOA and FireFly, 5.7% superior to grey wolf optimization and 10.3% superior to particle swarm optimization.

This paper develops the FR-WOA with optimized LSTM detecting and preventing black hole and wormhole attacks from WSN. To the best of the authors’ knowledge, this is the first work that uses FR-WOA with optimized LSTM detecting and preventing black hole and wormhole attacks from WSN.

This paper aims to model a technique that categorizes the texts from huge documents. The progression in internet technologies has raised the count of document accessibility, and thus the documents available online become countless. The text documents comprise of research article, journal papers, newspaper, technical reports and blogs. These large documents are useful and valuable for processing real-time applications. Also, these massive documents are used in several retrieval methods. Text classification plays a vital role in information retrieval technologies and is considered as an active field for processing massive applications. The aim of text classification is to categorize the large-sized documents into different categories on the basis of its contents. There exist numerous methods for performing text-related tasks such as profiling users, sentiment analysis and identification of spams, which is considered as a supervised learning issue and is addressed with text classifier.

At first, the input documents are pre-processed using the stop word removal and stemming technique such that the input is made effective and capable for feature extraction. In the feature extraction process, the features are extracted using the vector space model (VSM) and then, the feature selection is done for selecting the highly relevant features to perform text categorization. Once the features are selected, the text categorization is progressed using the deep belief network (DBN). The training of the DBN is performed using the proposed grasshopper crow optimization algorithm (GCOA) that is the integration of the grasshopper optimization algorithm (GOA) and Crow search algorithm (CSA). Moreover, the hybrid weight bounding model is devised using the proposed GCOA and range degree. Thus, the proposed GCOA + DBN is used for classifying the text documents.

The performance of the proposed technique is evaluated using accuracy, precision and recall is compared with existing techniques such as naive bayes, k-nearest neighbors, support vector machine and deep convolutional neural network (DCNN) and Stochastic Gradient-CAViaR + DCNN. Here, the proposed GCOA + DBN has improved performance with the values of 0.959, 0.959 and 0.96 for precision, recall and accuracy, respectively.

This paper proposes a technique that categorizes the texts from massive sized documents. From the findings, it can be shown that the proposed GCOA-based DBN effectively classifies the text documents.

The use of Internet of Things (IoT) and networks has built a potential impact on the product cost and time in a company’s manufacturing process. These IoT solutions provide end-to-end visibility and faster introduction of merchandise and supplier in the market. The main aim of this research paper is to supply products with improved quality and cheaper price, whereas the rising response and quality of the client service.

This paper designs and develops two cases for selecting the most efficient vendor while keeping in mind the profit and cost constraints in optimization.

Outsourcing is a vital parameter to cut back the price and maximize the profit of the manufacturer. Therefore, the integration of supply chain with IoT can provide a solution to the cost optimization and supplier/vendor selection problems in supply chain management.

The results show that the models are quite realistic and can help the IoT-based manufacturing units to make strategic decisions regarding product manufacturing and distribution.

The authors can further extend the model to derive the retailer’s profit function and develop the end product cost to the consumers and hence make it a n-level multi-vendor selection model for IoT-based systems.

The right choice of vendor for IoT-enabled business is a crucial concern. In this paper, the authors designed and developed multi-vendor models with in-house production and outsourcing decisions to meet the demand along with the vendor selection. The variable demands and designed variable unit cost function and batch order are set to make vendor selection more realistic.

This paper aims to draw lessons for telecommunications (telecom) network deployment in India, from a study of policy and regulatory approaches taken by other federal administrations in streamlining processes for site clearances, grant of rights of way (RoW) and approvals for local infrastructure deployment and sharing. With the urgent need for setting up small cells and rapid fiberisation of networks in the 5G era, the importance of such processes has gained prominence.

The authors adopt qualitative thematic content analysis with three-tier coding and classification to identify themes in archival and current documentary data and information obtained from subject-matter experts in the countries studied.

Formulation and implementation of telecom policy is led by national governments. However, national telecom administrations, in recognition of new needs, have co-opted states and local authorities as partners in development of telecom networks, providing the overall framework, guidance and appropriate incentives where required.

This cooperative model could work well in India, where telecom policy making and regulation is the prerogative of the central government, but administration of RoW and local clearances for cable laying, tower siting and associated infrastructure activities for expanding telecom networks are left to decentralised decision-making in the states and local bodies.

This research attempts to sytematise, thematise and draw cross-country comparisons to inform regulatory and administrative policy for 5G infrastructure rollout in India.

Introduction: Big data in the insurance industry can be defined as structured or unstructured data that can affect the rating, marketing, pricing, or underwriting. The five Vs of big data provide insurers with a valuable framework for converting their raw data into actionable information. These five Vs are specifically: (1) Volume: The need to look at the type of data and the internal systems; (2) Velocity: The speed at which big data is generated, collected, and refreshed; (3) Variety: Refers to both the structured and unstructured data; (4) Veracity: Refers to trustworthiness and confidence in data; and (5) Value: Refers to whether the data collected are good or bad.

Purpose: Insurance companies face many data challenges. However, the administration of big data has allowed insurers to acknowledge the demand of their customers and develop more personalised products. In addition, it can be used to make correct decisions about insurance operations such as risk selection and pricing.

Methodology: We do this by conducting a systematic literature review on big data. Our emphasis is on gathering information on the five Vs of the big data and the insurance market. Specifically, how big data can help in data-driven decisions.

Findings: Big data technology has created an endless series of opportunities, which have ensured a surge in its usage. It has helped businesses make the process more systematic, cost-effective, and helped in the reduction in fraud and risk prediction.

Mergers and acquisitions (M&As) are of three types: domestic, inbound and outbound cross-border. Inbound M&As provide an inflow of foreign funds into the economy, whereas outbound M&As involve the outflow of domestic funds. This paper examines the impact of domestic and cross-border mergers and acquisitions in Brazil on each other.

The authors analyze M&A activity in Brazil and examine the impact domestic, inbound and outbound M&As have on each other. The study uses a vector auto-regressive model to test the relationships for each quarter of 2000–2018. The M&A activity is operationalized using the total number of deals and the cumulative value of the deals in a particular period.

The results depict stark contrast for M&A activity measured through incidences and monetary value. Overall, the number of deals can better explain each other than value. The authors find that, in terms of incidences, domestic M&A is Granger caused by both outbound and inbound M&As together. Further, inbound and domestic M&As together Granger cause outbound M&As in terms of aggregate monetary value. The impulse response function reveals that incidence shocks created in M&A activity are longer lasting than the value shocks.

The results have implications for businesses and policymakers. The study reveals the complexities of crowding effects important for businesses. The government needs to structure its future investment-promotion strategies depending on the objectives related to the number and value of M&A activity.

The study uses econometric tools and empirical methods to find the unexplored nature of the relationship between domestic, outbound and inbound cross-border M&As.

This study aims to explore the relationship between institutional and macroeconomic factors and corporate financial flexibility while also investigating the moderating impact of selected board governance mechanisms on this relationship.

The sample of the study comprises 174 firms listed on the Colombo Stock Exchange for a period of eight years, from 2014 to 2021. Data were collected from secondary sources, and both descriptive and inferential statistical techniques were used for analyses.

Corporate financial flexibility is notably affected by profitability as an institutional factor and by gross domestic product growth rate and banking sector development as macroeconomic factors. Furthermore, the relationship between a company’s profitability and corporate financial flexibility is found to be moderated by selected board governance mechanisms. However, these governance mechanisms do not influence the relationship between corporate financial flexibility and other institutional factors (i.e. other than profitability) and macroeconomic factors considered in this study.

This study adds a fresh perspective to the existing body of knowledge in the field of corporate finance by emphasizing the interaction effect of board governance mechanisms on the association between macroeconomic and institutional variables and financial flexibility of firms. The findings are expected to be useful for business decision-makers in managing their corporate financial flexibility effectively and maximizing the use of their financial resources.

The paper develops a model for enhancing R&D productivity for Indian public funded laboratories. The paper utilizes the productivity data of five Council of Scientific and Industrial Research (CSIR) laboratories for analysis and to form the constructs of the model.

The weighted average method was employed for analyzing the rankings of survey respondents pertaining to the significant measures enhancing R&D involvement of researchers and significant non-R&D jobs. The authors have proposed a model of productivity. Various individual, organizational and environmental constructs related to the researchers working in the CSIR laboratories have been outlined that can enhance R&D productivity of researchers in Indian R&D laboratories. Partial Least Squares-Structural Equation Modeling (PLS-SEM) was used to find the predictability of the productivity model.

The organizational factors have a crucial role in enhancing the R&D outputs of CSIR laboratories. The R&D productivity of researchers can be improved through implementing the constructs of the proposed model of productivity.

The R&D productivity model can be adapted by the R&D laboratories to enhance researchers’ R&D involvement, increased R&D outputs and achieving self-sustenance in long run.

The R&D laboratories can initiate exercises to explore the most relevant factors and measures to enhance R&D productivity of their researchers. The constructs of the model can function as a guideline to introduce the most preferable research policies in the laboratory for overall mutual growth of laboratory and the researchers.

Hardly any studies have been found that have focused on finding the measures of enhancing R&D involvement of researchers and the influence of significant time-intensive jobs on researchers’ productivity.