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This paper aims to examine the dynamism of causal relationships among cloud computing (CC) adoption factors in the Indian context, considering the perspectives of both the cloud adopter and cloud provider.

The case-study method has been used to understand the dynamics among the factors. Using data from specific cases in India, causal loop diagrams (CLDs) have been developed. System dynamic modeling (SDM) and simulation are used to study the relationships and their effect on the adoption rate.

The results revealed that adoption of CC depends on various factors such as persuasion (time-saving, cost-saving and word of mouth) and constraint factors (security and financial loss). However, it is seen that the adoption rate is very sensitive to changes in adoption per contact and word of mouth. Further, the adopter firm has a quicker time to market, which gives an added advantage to the firm. Also, with CC services, a firm can fulfill its projects or clients' requirements with little to no upfront investment in information technology (IT) services.

Lack of security, standardization and undefined service-level agreements are a few pressing issues that make it difficult for firms to evaluate the performance and reliability of services. Hence, immediate attention is needed to make transparent policies on CC and its services, thereby building trust.

This is the first and only work that has tried to explore and empirically test the dynamics of critical factors while making an adoption decision, considering both the adopter and provider perspectives. This study shows the journey of a firm, starting from being a prospective adopter to an adopter and continuous user. The work also empirically tested how adopters of technology benefit from the technology.

Cloud computing (CC) services have given a tremendous boost to the creation of efficient and effective solutions. With developing countries at a slow pace of adoption, this research aims to identify factors and their interrelationships influencing the adoption of CC in a developing country context. The developing countries are enjoying numerous benefits from CC services; however, its low adoption is still a question in developing economies; hence, the authors have selected the context of information and communication technology (ICT) firms in India.

The qualitative research method is used where experts from thirteen ICT firms in India are interviewed.

Sixteen factors, twenty-eight subfactors, and 25 interrelationships are revealed through content analysis. Further, causal loop diagrams are proposed to display the behavior of cause and effect of these factors from a system's perspective. This will help to understand the relationships among the factors in order to enhance the speed of CC adoption. Possible financial loss and resistance to change are found as the key barriers to adoption. The proposed interrelationships can guide both policymakers and service providers for designing effective CC policies.

This is the first scholarly work that identifies interrelationships among factors and subfactors, thereby providing a holistic picture to decision-makers while making a choice on whether to adopt cloud services or continue with on premise data centers and servers.

The purpose of this paper is to presents an analysis of geographically and disciplinary scattered academic publications of cloud computing (CC) research in information systems. This review aims to understand the research methodology, research frameworks and models, geographical distribution, trends, critical factors and causal relationships associated with cloud computing adoption (CCA).

Systematic-literature-review using natural language processing is conducted to explore the phenomenon. The relevant research studies are extracted from various online databases using quality-assessment-criteria.

The study is a novel attempt to highlight the differences in critical factors for CCA in different country-settings. Further, the research explores the causal relationships among the identified factors. The findings of this 12-year systematic-review contribute by aiding the providers and potential adopters to devise context-specific strategies for the penetration of cloud services and sound adoption decisions (ADs), respectively. The findings also highlight the prospective avenues of research in the domain for researchers. Using the in-depth analysis, conceptual frameworks have been proposed that can assist in exploring the pre-adoption and post-adoption of CC.

This study contributes to CCA research by providing holistic insights into the methodology, research framework and models, geographical focus, critical factors and causal relationships influencing the AD or intention. The review highlights the unexplored emerging research topics in the field of CCA for future research directions.

Project complexity (PC) governs project success, but the project management literature primarily focuses on performance measures and rarely examines the complexity factors, especially for megaprojects. This paper aims to determine the most significant complexity factors for the railway megaprojects in India.

A mixed approach using the Delphi and best–worst method (BWM) helped to identify, validate and determine the most critical factors that require intervention to diminish variance from project performance.

The BWM resulted in stakeholder management, followed by organizational and technological complexity as significant complexity factors, and the varied interests of the stakeholder as the most important among the 40 subfactors.

The finding indicates the necessity for strategic, tactical and operational-level interventions to effectively manage the complexity affecting project efficiency because of the varied stakeholders. This paper will guide the project and general managers to prioritize their resources to handle complexity for effective project performance measured in terms of time, cost and quality and help them make strategic decisions. The research findings of this study are expected to help researchers and practitioners in better planning and smoother execution of projects. In addition, this study would help the researchers formulate policies and strategies for better handling of the projects.

This study adds significant value to the body of knowledge related to PC in megaprojects in developing countries. The result of the investigation underlined that nine complexity factors and seven unique subfactors, namely, the sustainable environment, timely availability of information, communication in both directions, interdepartmental dependency and coordination, design, statutory norms, site challenges, socioeconomic conditions, the tendency of staff to accept new technology and the frequent changes in the requirements of stakeholders are significant in railway megaprojects. The BWM is applied to rank the complexity factors and subfactors in the case area.

Casting is one of the well-known manufacturing processes to make durable parts of goods and machinery. However, the quality of the casting parts depends on the proper choice of process variables related to properties of the materials used in making a mold and the product itself; hence, variables related to product/process designs are taken into consideration. Understanding casting techniques considering significant process variables is critical to achieving better quality castings and helps to improve the productivity of the casting processes. This study aims to understand the computational models developed for achieving better quality castings using various casting techniques.

A systematic literature review is conducted in the field of casting considering the period 2000–2020. The keyword co-occurrence network and word cloud from the bibliometric analysis and text mining of the articles reveal that optimization and simulation models are extensively developed for various casting techniques, including sand casting, investment casting, die casting and squeeze casting, to improve quality aspects of the casting's product. This study further investigates the optimization and simulation models and has identified various process variables involved in each casting technique that are significantly affecting the outcomes of the processes in terms of defects, mechanical properties, yield, dimensional accuracy and emissions.

This study has drawn out the need for developing smart casting environments with data-driven modeling that will enable dynamic fine-tuning of the casting processes and help in achieving desired outcomes in today's competitive markets. This study highlights the possible technology interventions across the metal casting processes, which can further enhance the quality of the metal casting products and productivity of the casting processes, which show the future scope of this field.

This paper investigates the body of literature on the contributions of various researchers in producing high-quality casting parts and performs bibliometric analysis on the articles. However, research articles from high-quality journals are considered for the literature analysis in identifying the critical parameters influencing quality of metal castings.

The systematic literature review reveals the analytical models developed using simulation and optimization techniques and the important quality characteristics of the casting products. Further, the study also explores critical influencing parameters involved in every casting process that significantly affects the quality characteristics of the metal castings.

Foundry produces cast metal components and parts for various industries and drives manufacturing excellence all over the world. Assuring quality of these components and parts is vital for the end product quality. The complexity in foundry operations increases with the complexity in designs, patterns and geometry and the quality parameters of the casting processes need to be monitored, evaluated and controlled to achieve expected quality levels.

The literature addresses quality improvement in foundry industry primarily focusing on surface roughness, mechanical properties, dimensional accuracy and defects in the cast parts and components which are often affected by numerous process variables. Primary data are collected from the experts working in sand and investment casting processes. The authors perform machine learning analysis of the data to model the quality parameters with appropriate process variables. Further, cluster analysis using k-means clustering method is performed to develop clusters of correlated process variables for sand and investment casting processes.

The authors identified primary process variables determining each quality parameter using machine learning approach. Quality parameters such as surface roughness, defects, mechanical properties and dimensional accuracy are represented by the identified sand-casting process variables accurately up to 83%, 83%, 100% and 83% and are represented by the identified investment-casting process variables accurately up to 100%, 67%, 67% and 100% respectively. Moreover, the prioritization of process variables in influencing the quality parameters is established which further helps the practitioners to monitor and control them within acceptable levels. Further the clusters of process variables help in analyzing their combined effect on quality parameters of casting products.

This study identified potential process variables and collected data from experts, researchers and practitioners on the effect of these on the quality aspects of cast products. While most of the previous studies focus on a very limited process variables for enhancing the quality characteristics of cast parts and components, this study represents each quality parameter as the function of influencing process variables which will enable the quality managers in Indian foundries to maintain capability and stability of casting processes. The models hence developed for both sand and investment casting for each quality parameter are validated with real life applications. Such studies are scarcely reported in the literature.

In today’s environment, electric vehicles (EVs) industries and services provided to consumers are facing many challenges. The public in large numbers is not adopting Electric Vehicles because of the unavailability of suitable EVs and not getting proper charging infrastructure to the consumers. The purpose of this study and research work is to analyze the condition of this industry, its charging infrastructure and E-government policies. Based on the above analysis, frameworks/models are to be designed to assist organizations in formulating strategies for the EV industry and providing proper infrastructure to the consumer.

The Variables for the EV and EVsCI case study are identified from an exhaustive literature review, stakeholders’ perspectives, experts’ opinions, existing EV policies, future policy blueprints and data available for this industry, applying the Situation Actor Process-Learning Action Performance (SAP-LAP) methodology. After that, the Interpretive Ranking Process (IRP) is integrated with the SAP-LAP approach to develop models.

The rankings of Actors Versus Processes and Actions Versus Performances have been obtained and then models have been developed. These models can serve policymakers in framing and implementing effective policies.

Although the SAP-LAP approach is an innovative approach for identifying variables, however, this approach lacks perfect interactive relationships among the variables. It may lead to imperfect decision-making. To avoid this, the IRP approach is integrated with the SAP-LAP approach which provides more accurate models for analysis and providing recommendations to government and organizations.

Entrepreneurial intention is crucial in both entrepreneurship literature and practice. However, limited information exists regarding entrepreneurs’ intentions in the realm of social enterprise creation. This study aims to determine the social entrepreneurial intentions of Generation Z students within the Indian environment by utilising the theory of planned behaviour (TPB) and its applicability using personality factors like risk-taking ability, proactivity, empathy and emotional intelligence.

A survey method was used, and data was collected by Generation Z individuals at three public universities in India using a self-administered questionnaire. The study used a purposive sampling technique, and further, the model was tested using the partial least square-structural equation modelling technique.

The study findings supported the proposed model of extended TPB, demonstrating that factors such as emotional intelligence, risk-taking ability and proactiveness are found prominent in Indian students. Furthermore, the role of gender as a control variable reflected no effect on social entrepreneurial intentions.

This research enhances the literature on social entrepreneurship by offering novel insights into social entrepreneurial intention. It is among the pioneers of its type to examine social entrepreneurial intent concerning personality factors among students of higher education institutions. This study provides interesting insights for universities and policymakers since they play a crucial role in promoting these antecedents through education and stimulation by enhancing entrepreneurial culture in developing countries.

The aim of the study is to examine the influence of Intellectual Capital (IC) and its components on the financial performance of Indian sugar mill companies.

The present study follows the quantitative research, and uses data from Indian sugar mill companies over the period of recent 10 years. The Modified Value- Added Intellectual Capital (MVAIC) method is employed to evaluate IC. Authors construct panel regression models to test the hypotheses where Return on Equity (RoE) and Return on Asset (RoA) were considered as a representation of financial performance (dependent variable) and IC has been considered as the independent variable along with control variables.

The findings reveal that IC components show greater explanatory power than aggregate IC and MVAIC has a positive relationship with firm performance. It is evident that Capital Employed Efficiency (CEE) and Relational Capital Efficiency (RCE) have a positive effect on the RoA, while Human Capital Efficiency (HCE) and CEE have a positive impact on RoE. CEE is found to be a highly significant component to explain the financial performance of Indian sugar mill firms.

The study has practical implications for the policymakers for effective utilization of IC resources for worth enhancement which is essential for the improvement of financial performance.

The research extends the literature of IC by linking it to the financial performance of Indian sugar mill industry.

This study investigates how tertiary education in Bangladesh can promote gender equality by integrating feminine writing into the academic environment. It examines feminine writing as a vehicle for empowering female students, encouraging critical thinking and challenging societal norms that perpetuate gender inequality.

This study follows a quantitative research methodology. A cross-sectional survey was conducted among 672 students from various educational institutions across Bangladesh. Based on a research framework driven by critical pedagogy theory, the data analysis employed descriptive and inferential statistics to explore relationships among variables.

The findings indicate that 76.8% of female students engage in personal narratives and 54.9% in feminist essays, showing a significant preference for feminine writing. Female students (70.1%) exhibit greater awareness of gender issues than male students (29.9%). Arts and social sciences students show the highest engagement in gender discussions (52.7%) and are more likely to perceive supportive environments for feminine writing (65.9%). However, students in vocational fields report only 15.5% engagement in gender-related discussions and significantly lower institutional support. These disparities highlight the need for inclusive academic frameworks encouraging broader participation in gender discourse across all disciplines.

The study emphasizes embedding feminine writing and gender perspectives across curricula to foster gender equality in Bangladesh’s higher education sector. To create a more supportive academic environment, policymakers and educational institutions must integrate gender-related content across all disciplines, particularly in science, business, vocational and technical fields.

As one of the first studies to examine the intersection of tertiary education, feminine writing and gender equality in Bangladesh, this research offers new insights into the role of higher education in fostering gender empowerment and critical engagement with societal norms. The findings contribute to a broader understanding of how academic settings can serve as transformative spaces for promoting gender equality and inclusive self-expression.