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.

Modeling competing failure modes is an important problem in engineering and survival analyses. Competing failure modes are partially observed in many applications and often pose a modeling challenge. This study discusses the inference for partially observed failure modes assuming a Burr XII distribution. In particular, we consider two failure modes, and the failure time data are collected under a hybrid type I censoring scheme. The model parameters are estimated using maximum likelihood and Bayesian methods under a symmetric squared error loss function, whereas the intervals estimation is done with three methods: asymptotic and credible confidence intervals. Besides a simulation study, a real-life data set is taken from individuals who live in an environment with several diseases to present the utility of the work. Additionally, a simulation study is constructed to measure and compare different estimation methods.

Many researchers employed Poisson distribution-based control charts to monitor count data. Nevertheless, these charts can handle count data that deviate from the Poisson assumption of equal mean and variance. This paper suggests a new control chart (CC) that uses the generalized Conway–Maxwell–Poisson (GCOMP) distribution, which can deal with count data that have different levels of dispersion and zero-inflation (ZI). The proposed chart is designed considering the total number of counts. The main advantage of this study is that it pays attention to the tails of the count data when monitoring the process. The performance is measured by the average run length using L control limits at different sample sizes and parametric settings. The findings demonstrate that, for count data with varying tail behaviors, the proposed chart performs better compared to existing CCs. ZI count data can also be monitored with the proposed chart. The proposed chart can be applied in a variety of fields, as verified by the examples provided in this paper.

The development of bivariate distributions is a challenging area of research, and a novel class of bivariate distributions is proposed in this paper. Certain useful mathematical characteristics of the proposed class of bivariate distributions are explored in general. These properties include the joint and conditional and joint survival function moments and measures of dependence. The parameters of the proposed class of distributions are estimated using the maximum likelihood method of estimation. A multivariate version of the proposed class is also proposed, and some necessary properties are discussed. The multivariate dependence measures are obtained for the proposed multivariate class of distributions. A new bivariate power function distribution is proposed using the power function distribution as a baseline distribution in the proposed bivariate class. Some useful properties of the proposed bivariate distribution are studied. The parameters of the proposed bivariate distribution are estimated using the maximum likelihood method. An extensive simulation study has been conducted to see the performance of the estimation method. The new bivariate power function distribution is used to model some real data.

Modelling in lifetime phenomena is a significant issue in many scientific areas. Sometimes many standard models lack superiority in modelling data set. Designing a new form of probability distribution by using different techniques has received a widely attention in statistical theory in the recent years. In this paper, we formulated exponentiated exponential Rayleigh (EER) distribution. We discussed the reliability and hazard rate functions of the EER distribution and computed the quantile, median, skewness and kurtosis. Moreover, the correlations between the parameters and the median, skewness, and kurtosis are investigated. Bayesian and non-Bayesian approaches are adopted to estimate the unknown parameters of EER distribution. In Bayesian approach, we are used Markov Chen Monte Carlo (MCMC) method to obtain the approximate Bayes point estimate. The proposed distribution is used to analyze, light-emitting diodes data, strength of glass fibers data and Wheaton River data. The estimation results of the EER distribution are compared with exponential Rayleigh, Rayleigh, and exponential distributions.