Evolving from an ethical consideration to a strategic imperative, green innovation (GI) compels businesses to continually enhance their processes to achieve sustainable growth. Based on bibliometric analysis of 594 Web of Science (WOS)-sourced articles from 2000 to 2023 using VOSviewer-1.6.20 and Bibliometrix-4.3.0, this study sheds light on the existing trends of green innovation, its contributors, and potential future directions in today’s business landscape. Our findings unveil significant insights from GI literature; an upward growth trajectory in publications; limited collaboration among researchers and institutions (notable collaborative networks among countries include China, Spain, and the United Kingdom); and trending GI terms and themes include green intellectual capital, GI efficiency, green product innovation, green absorptive capacity, green knowledge acquisition, big data, etc. These insights serve as a comprehensive guide for practitioners and scholars navigating the study of GI within the business and management sphere.

In this article, we present a new generalization of weighted Weibull distribution using Topp Leone family of distributions. We have studied some statistical properties of the proposed distribution including quantile function, moment generating function, probability generating function, raw moments, incomplete moments, probability, weighted moments, Rayeni and q th entropy. The have obtained numerical values of the various measures to see the eect of model parameters. Distribution of of order statistics for the proposed model has also been obtained. The estimation of the model parameters has been done by using maximum likelihood method. The eectiveness of proposed model is analyzed by means of a real data sets. Finally, some concluding remarks are given.

The inverse Weibull distribution which was first introduced to investigate the decay of mechanical components is widely used in the field of biology, especially in the areas of survival and reliability analysis. In this paper, a generalisation of inverse Weibull distribution is presented using a new class of gamma distributions. For the proposed model, important mathematical properties including quantile function, noncentral moment, central moment, moment generating function, characteristics function, and general coefficient are discussed. The shapes of the probability density function and risk function were obtained and the graphical demonstrations of them were provided. To estimate model parameters of the proposed method, maximum likelihood was used. An illustrative simulation study was conducted to observe how the maximum likelihood estimators behave for different sample sizes and for different values of parameters. Two real data sets from the field of biomedical sciences were used to demonstrate the practical applications of the proposed model in real life phenomena. Results were obtained from the data sets, using the gamma inverse Weibull distribution and some known existing distributions and compared. The comparative analysis justified that the proposed model significantly fits with the survival data compared to other distributions.